
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import plotly.express as px
import plotly.graph_objects as go
import seaborn as sns
sns.set()
%matplotlib notebook


df_GDP = pd.read_csv("Project2/GDP_growth_rate.csv")
df_Murder = pd.read_csv("Project2/murder_total_deaths.csv")
df_Population = pd.read_csv("Project2/population_total.csv")
df_Income = pd.read_csv("Project2/income_per_person_gdppercapita_ppp_inflation_adjusted.csv")
df_Unemployment = pd.read_csv("Project2/Unemployment_rate.csv")


df_GDP.head()


df_Murder.head()


df_Population.head()


df_Income.head()


df_Unemployment.head()


def replacing(df):
    #First i replaced all K, M , B with empty space , Then i extracted all the K, M , B to its amount in numbers and multiply it in the number that had them..
    for values in df.columns[1:]:
        df[values] = df[values].replace(r'[kMB]+$', '', regex=True).astype(float) * df[values].str.extract(r'[\d\.]+([kMB]+)', expand=False).fillna(1).replace(['k','M',"B"], [10**3, 10**6, 10**9]).astype(int)
    
    return df


years = df_Income.columns[df_Income.columns.get_loc("1990"):df_Income.columns.get_loc("2016")+1]
def filtering_years(df):
    for year in df.columns:
        if year not in years and year not in ["country", "Country"]:
            df.drop(year, axis=1, inplace=True)
    
    return df


ye2 = [2000 , 2005 , 2010 , 2016]
def Comparing(df):
    new_df = df[(df.index == "2000") | (df.index == "2005") | (df.index == "2010") | (df.index == "2016")]
            
    return new_df


sum(df_Population.isnull().sum())

0


sum(df_Income.isnull().sum())

0


sum(df_Murder.isnull().sum())

0


sum(df_GDP.isnull().sum())

4073


sum(df_Unemployment.isnull().sum())

9176


df_GDP.fillna(0,inplace=True)


df_Unemployment.fillna(0, inplace=True)


sum(df_GDP.isnull().sum())

0


sum(df_Unemployment.isnull().sum())

0


filtering_years(df_GDP)


filtering_years(df_Unemployment)


filtering_years(df_Population)


filtering_years(df_Murder)


filtering_years(df_Income)


replacing(df_Income)


replacing(df_Murder)


replacing(df_Population)


murder_ratio = df_Population.join(df_Murder,rsuffix="_Murder")


murder_ratio


# Checking the dimension of the murder ratio data... 
murder_ratio.shape

(197, 56)


#Putting the countries in the population data in population variable,  and putting the countries in the total murder data to the murder variable   ...
population = murder_ratio["country"].sort_values().values


murder = murder_ratio["country_Murder"].sort_values().values


population

array(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',
       'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia',
       'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh',
       'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan',
       'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',
       'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia',
       'Cameroon', 'Canada', 'Cape Verde', 'Central African Republic',
       'Chad', 'Chile', 'China', 'Colombia', 'Comoros',
       'Congo, Dem. Rep.', 'Congo, Rep.', 'Costa Rica', "Cote d'Ivoire",
       'Croatia', 'Cuba', 'Cyprus', 'Czech Republic', 'Denmark',
       'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',
       'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',
       'Eswatini', 'Ethiopia', 'Fiji', 'Finland', 'France', 'Gabon',
       'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Grenada',
       'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti',
       'Holy See', 'Honduras', 'Hong Kong, China', 'Hungary', 'Iceland',
       'India', 'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',
       'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati',
       'Kuwait', 'Kyrgyz Republic', 'Lao', 'Latvia', 'Lebanon', 'Lesotho',
       'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg',
       'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta',
       'Marshall Islands', 'Mauritania', 'Mauritius', 'Mexico',
       'Micronesia, Fed. Sts.', 'Moldova', 'Monaco', 'Mongolia',
       'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia',
       'Nauru', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua',
       'Niger', 'Nigeria', 'North Korea', 'North Macedonia', 'Norway',
       'Oman', 'Pakistan', 'Palau', 'Palestine', 'Panama',
       'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland',
       'Portugal', 'Qatar', 'Romania', 'Russia', 'Rwanda', 'Samoa',
       'San Marino', 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal',
       'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',
       'Slovak Republic', 'Slovenia', 'Solomon Islands', 'Somalia',
       'South Africa', 'South Korea', 'South Sudan', 'Spain', 'Sri Lanka',
       'St. Kitts and Nevis', 'St. Lucia',
       'St. Vincent and the Grenadines', 'Sudan', 'Suriname', 'Sweden',
       'Switzerland', 'Syria', 'Taiwan', 'Tajikistan', 'Tanzania',
       'Thailand', 'Timor-Leste', 'Togo', 'Tonga', 'Trinidad and Tobago',
       'Tunisia', 'Turkey', 'Turkmenistan', 'Tuvalu', 'Uganda', 'Ukraine',
       'United Arab Emirates', 'United Kingdom', 'United States',
       'Uruguay', 'Uzbekistan', 'Vanuatu', 'Venezuela', 'Vietnam',
       'Yemen', 'Zambia', 'Zimbabwe'], dtype=object)


murder

array(['Afghanistan', 'Albania', 'Algeria', 'American Samoa', 'Andorra',
       'Angola', 'Antigua and Barbuda', 'Argentina', 'Armenia',
       'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',
       'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin',
       'Bermuda', 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina',
       'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'Burkina Faso',
       'Burundi', 'Cambodia', 'Cameroon', 'Canada', 'Cape Verde',
       'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia',
       'Comoros', 'Congo, Dem. Rep.', 'Congo, Rep.', 'Costa Rica',
       "Cote d'Ivoire", 'Croatia', 'Cuba', 'Cyprus', 'Czech Republic',
       'Denmark', 'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador',
       'Egypt', 'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',
       'Eswatini', 'Ethiopia', 'Fiji', 'Finland', 'France', 'Gabon',
       'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Greenland',
       'Grenada', 'Guam', 'Guatemala', 'Guinea', 'Guinea-Bissau',
       'Guyana', 'Haiti', 'Honduras', 'Hungary', 'Iceland', 'India',
       'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',
       'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati',
       'Kuwait', 'Kyrgyz Republic', 'Lao', 'Latvia', 'Lebanon', 'Lesotho',
       'Liberia', 'Libya', 'Lithuania', 'Luxembourg', 'Madagascar',
       'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta',
       'Marshall Islands', 'Mauritania', 'Mauritius', 'Mexico',
       'Micronesia, Fed. Sts.', 'Moldova', 'Mongolia', 'Montenegro',
       'Morocco', 'Mozambique', 'Myanmar', 'Namibia', 'Nepal',
       'Netherlands', 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria',
       'North Korea', 'North Macedonia', 'Northern Mariana Islands',
       'Norway', 'Oman', 'Pakistan', 'Palestine', 'Panama',
       'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland',
       'Portugal', 'Puerto Rico', 'Qatar', 'Romania', 'Russia', 'Rwanda',
       'Samoa', 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal',
       'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',
       'Slovak Republic', 'Slovenia', 'Solomon Islands', 'Somalia',
       'South Africa', 'South Korea', 'South Sudan', 'Spain', 'Sri Lanka',
       'St. Lucia', 'St. Vincent and the Grenadines', 'Sudan', 'Suriname',
       'Sweden', 'Switzerland', 'Syria', 'Taiwan', 'Tajikistan',
       'Tanzania', 'Thailand', 'Timor-Leste', 'Togo', 'Tonga',
       'Trinidad and Tobago', 'Tunisia', 'Turkey', 'Turkmenistan',
       'Uganda', 'Ukraine', 'United Arab Emirates', 'United Kingdom',
       'United States', 'Uruguay', 'Uzbekistan', 'Vanuatu', 'Venezuela',
       'Vietnam', 'Yemen', 'Zambia', 'Zimbabwe', nan, nan, nan],
      dtype=object)


# I used for loop to go through the countries in the population list , then if this country not in the countries murder list . i will remove it from the data ...
for country in population:
    if country not in murder:
        population[population != country]

# Using the countries in the total murder as an index for the data..
murder_ratio.set_index("country_Murder", inplace = True)


# removing the column that have population countries ...
murder_ratio.drop(columns="country",inplace=True)


#Reseting the index to use numbers instead of the Countries in the total murder ...
murder_ratio.reset_index(inplace=True)


# Getting the Years that have Murder_counts in it and seperate it from the population years...
murder_columns_list = murder_ratio.columns

murder_years = murder_ratio.columns[murder_columns_list.get_indexer(["1990_Murder"])[0]:]

population_years = murder_ratio.columns[1:murder_columns_list.get_indexer(["1990_Murder"])[0]]


population_years

Index(['1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998',
       '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007',
       '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016'],
      dtype='object')


# Comparing the length of two list to make sure that we have the same years ..
len(population_years)

27


len(murder_years)

27


# Getting the Murder ratio by dividing the total murder on the population number then multiplaying it with 100k to get it per 100k population ...Then i add the result in a new Columns and label them with the number of the year + Per 100k ..
for i in range(0,len(population_years)):
    murder_ratio["{}_Per 100k".format(population_years[i])] = (murder_ratio[murder_years[i]] / murder_ratio[population_years[i]]) * 100000


murder_ratio


#Removing the other years coulmns that doesnt have Per 100k since we only need the raito..
for i in range(0,len(population_years)):
    murder_ratio.drop(population_years[i], axis=1,inplace=True)
    murder_ratio.drop(murder_years[i], axis=1,inplace=True)


murder_ratio


# Changing country_Murder_counts to Country since this is the only column that have countries in it ....
murder_ratio.columns = murder_ratio.columns.str.replace(r'_[^_]*$', '',regex=True)


murder_ratio


murder_ratio = murder_ratio[0:194]


sum(murder_ratio.isnull().sum())

0


murder_ratio


# new_murder = countries(murder_ratio)


murder_ratio.set_index("country", inplace = True)


murder_ratio = murder_ratio.T


murder_ratio.columns.rename(name="Years",inplace=True) # --> Renaming the country name to Years...


murder_ratio


df_GDP.set_index("Country",inplace=True)


df_GDP


df_GDP = df_GDP.T


df_GDP


df_GDP.columns.rename(name="Years",inplace=True) # --> Renaming the country name to Years...


df_GDP


df_Income.set_index("country",inplace=True) # Making the Country column my indeces ...


df_Income


df_Income = df_Income.T # Transposing the columns with the indeces


df_Income


df_Income.columns.rename(name="Years",inplace=True)


df_Income


df_Unemployment.set_index("Country",inplace=True)


df_Unemployment = df_Unemployment.T


df_Unemployment.columns.rename(name="Years",inplace=True)


df_Unemployment


df_Population.set_index("country", inplace = True)


df_Population = df_Population.T


df_Population.columns.rename(name="Years",inplace=True)


df_Population


plt.figure(figsize=(15,10), dpi= 65)
plt.vlines(x=comparing_Egypt_Murder.index, ymin=0, ymax=comparing_Egypt_Murder.values,label='Egypt',color="#66995A")
for x, y in zip(comparing_Egypt_Murder.index, comparing_Egypt_Murder.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'red' if y > 1.2 else '#098003', 'size':14})
plt.plot(comparing_Egypt_Murder.index, comparing_Egypt_Murder.values,'o--', color='#1A1429', alpha=0.5)
# Decorations    
# plt.yticks(df.index, df.cars, fontsize=12)
# plt.xlim(-2.5, 2.5)
plt.grid(axis = 'y', linestyle=':', alpha=1,color = 'black')
title_size = 18
plt.title("Egypt Murder Rate" ,fontsize=title_size)
plt.legend(loc="upper left",fontsize= 15)
plt.ylabel("Rate",fontsize=title_size)
plt.xlabel("Years", fontsize=title_size)
plt.tick_params(labelsize=16,length=0)
plt.tight_layout()
plt.show()


Average_Egypt_Murder = (0.83 + 1.04 + 1.3 + 1.33)/ 4
Average_Egypt_Murder

1.125


murder_ratio.Egypt.describe()

count    27.000000
mean      1.029824
std       0.218151
min       0.798995
25%       0.818540
50%       0.953052
75%       1.301601
max       1.326180
Name: Egypt, dtype: float64


comparing_Japan_Murder = Comparing(murder_ratio.Japan)
plt.figure(figsize=(15,10), dpi= 65)
plt.vlines(x=comparing_Japan_Murder.index, ymin=0, ymax=comparing_Japan_Murder.values,label='Japan',color="#4D2403")
for x, y in zip(comparing_Japan_Murder.index, comparing_Japan_Murder.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#c91a22' if y > 2.5 else '#098003', 'size':14})
plt.plot(comparing_Japan_Murder.index, comparing_Japan_Murder.values,'o--', color='#BA631C', alpha=0.5)
plt.grid(axis = 'y', linestyle=':', alpha=1,color = 'black')
title_size = 18
plt.title("Japan Murder Rate" ,fontsize=title_size)
plt.legend(loc="upper right",fontsize= 15)
plt.ylabel("Rate",fontsize=title_size)
plt.xlabel("Years", fontsize=title_size)
plt.tick_params(labelsize=16,length=0)
plt.tight_layout()
plt.show()


Average_Japan_Murder = (3.5 + 2.79 + 2.13 + 1.64)/ 4
Average_Japan_Murder

2.515


murder_ratio.Japan.describe()

count    27.000000
mean      3.084479
std       1.011557
min       1.637475
25%       2.182900
50%       3.083573
75%       3.845321
max       4.894515
Name: Japan, dtype: float64


comparing_Russia_Murder = Comparing(murder_ratio.Russia)
plt.figure(figsize=(15,10), dpi= 65)
plt.vlines(x=comparing_Russia_Murder.index, ymin=0, ymax=comparing_Russia_Murder.values,label='Russia',color="#6010BB")
for x, y in zip(comparing_Russia_Murder.index, comparing_Russia_Murder.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#c91a22' if y > 150 else '#098003', 'size':14})
plt.plot(comparing_Russia_Murder.index, comparing_Russia_Murder.values,'o--', color='#63B00C', alpha=0.5)
plt.grid(axis = 'y', linestyle=':', alpha=1,color = 'black')
title_size = 18
plt.title("Russia Murder Rate" ,fontsize=title_size)
plt.legend(loc="upper right",fontsize= 15)
plt.ylabel("Rate",fontsize=title_size)
plt.xlabel("Years", fontsize=title_size)
plt.tick_params(labelsize=16,length=0)
plt.tight_layout()
plt.show()


Average_Russia_Murder = (210.41 + 193.93 + 136.59 + 124.24)/ 4
Average_Russia_Murder

166.29250000000002


murder_ratio.Russia.describe()

count     27.000000
mean     168.081397
std       38.555582
min      108.085106
25%      126.653232
50%      175.471698
75%      203.425224
max      223.478261
Name: Russia, dtype: float64


comparing_Afghanistan_Murder = Comparing(murder_ratio.Afghanistan)
plt.figure(figsize=(15,10), dpi= 65)
plt.vlines(x=comparing_Afghanistan_Murder.index, ymin=0, ymax=comparing_Afghanistan_Murder.values,label='Afghanistan',color="#36110A")
for x, y in zip(comparing_Afghanistan_Murder.index, comparing_Afghanistan_Murder.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#c91a22' if y > 17.26 else '#098003', 'size':14})
plt.plot(comparing_Afghanistan_Murder.index, comparing_Afghanistan_Murder.values,'o--', color='#6B1F15', alpha=0.5)
plt.grid(axis = 'y', linestyle=':', alpha=1,color = 'black')
title_size = 18
plt.title("Afghanistan Murder Rate" ,fontsize=title_size)
plt.legend(loc="upper right",fontsize= 15)
plt.ylabel("Rate",fontsize=title_size)
plt.xlabel("Years", fontsize=title_size)
plt.tick_params(labelsize=16,length=0)
plt.tight_layout()
plt.show()


Average_Afghanistan_Murder = (17.26 + 18.48 + 16.92 + 17.71)/ 4
Average_Afghanistan_Murder

17.5925


murder_ratio.Afghanistan.describe()

count    27.000000
mean     17.288615
std       0.661903
min      16.413793
25%      16.689316
50%      17.213622
75%      17.683366
max      18.502024
Name: Afghanistan, dtype: float64


comparing_USA_Murder = Comparing(murder_ratio["United States"])
plt.figure(figsize=(15,10), dpi= 65)
plt.vlines(x=comparing_USA_Murder.index, ymin=0, ymax=comparing_USA_Murder.values,label='United States',color="#36110A")
for x, y in zip(comparing_USA_Murder.index, comparing_USA_Murder.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#c91a22' if y > 60 else '#098003', 'size':14})
plt.plot(comparing_USA_Murder.index, comparing_USA_Murder.values,'o--', color='#6B1F15', alpha=0.5)
plt.grid(axis = 'y', linestyle=':', alpha=1,color = 'black')
title_size = 18
plt.title("United States Murder Rate" ,fontsize=title_size)
plt.legend(loc="upper right",fontsize= 15)
plt.ylabel("Rate",fontsize=title_size)
plt.xlabel("Years", fontsize=title_size)
plt.tick_params(labelsize=16,length=0)
plt.tight_layout()
plt.show()


Average_USA_Murder = (76.79 + 67.15 + 54.01 + 46.21)/ 4
Average_USA_Murder

61.04


murder_ratio["United States"].describe()

count     27.000000
mean      76.649519
std       25.549383
min       46.212121
25%       55.350122
50%       71.153846
75%       92.327189
max      128.160920
Name: United States, dtype: float64


comparing_China_Murder = Comparing(murder_ratio.China)
plt.figure(figsize=(15,10), dpi= 65)
plt.vlines(x=comparing_China_Murder.index, ymin=0, ymax=comparing_China_Murder.values,label='China',color="#36110A")
for x, y in zip(comparing_China_Murder.index, comparing_China_Murder.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#c91a22' if y > 150 else '#098003', 'size':14})
plt.plot(comparing_China_Murder.index, comparing_China_Murder.values,'o--', color='#6B1F15', alpha=0.5)
plt.grid(axis = 'y', linestyle=':', alpha=1,color = 'black')
title_size = 18
plt.title("China Murder Rate" ,fontsize=title_size)
plt.legend(loc="upper right",fontsize= 15)
plt.ylabel("Rate",fontsize=title_size)
plt.xlabel("Years", fontsize=title_size)
plt.tick_params(labelsize=16,length=0)
plt.tight_layout()
plt.show()


Average_China_Murder = (283.23 + 177.4 + 115.27 + 78.24)/ 4
Average_China_Murder

163.535


murder_ratio.China.describe()

count     27.000000
mean     229.156154
std      115.507716
min       78.242678
25%      120.514256
50%      213.095238
75%      352.682318
max      392.372881
Name: China, dtype: float64


df_Murder.set_index("country", inplace = True)


df_Murder = df_Murder.T


df_Murder.columns.rename(name="Years",inplace=True)


murder = {}
for country in df_Murder.columns:
    murder[df_Murder[country].mean()] = country


print("The country with the most Murder Rate is {} with an average of --> {} ....".format(murder[max(murder.keys())],max(murder.keys())))

The country with the most Murder Rate is Brazil with an average of --> 52555.555555555555 ....


print("The country with the Least Murder Rate is {} with an average of --> {} ....".format(murder[min(murder.keys())],min(murder.keys())))

The country with the Least Murder Rate is Andorra with an average of --> 0.5137037037037038 ....


comparing = df_Population[(df_Population.index == "2000") | (df_Population.index == "2005") | (df_Population.index == "2010") | (df_Population.index == "2016")]

fig,ax = plt.subplots(figsize=(15,10),dpi=70)
splot = sns.barplot(x=ye2,y=comparing.Egypt,data=comparing,ci=95,ax=ax,palette = "viridis")
for p in splot.patches:
    if p.get_height() > 0: # -- > i used if statments bcz when i try to use annotators , there is some countries that have negative values , so the position of the value gets in the bar.. 
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, 9), 
                       textcoords = 'offset points')
    if p.get_height() < 0 : # --> i had to seperate the positive values from the negative values and give each ot them a different axis...
        splot.annotate(format(p.get_height(), ".1f"), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, -11), # ---> Negative y axis since its a negative value ...
                       textcoords = 'offset points')

for patch in ax.patches :
        current_width = patch.get_width()
        diff = current_width - 0.1

        # we change the bar width
        patch.set_width(.1)

        # we recenter the bar
        patch.set_x(patch.get_x() + diff * .5)
        
        
title_size = 18
ax.set_title("Egypt Population" ,fontsize=title_size)
ax.set_ylabel("Count",fontsize=title_size)
ax.tick_params(labelsize=16,length=0)
plt.tight_layout()


Egyption_Population = 94400000 - 68800000
Egyption_Population

25600000


comparing.Egypt.describe()

count    4.000000e+00
mean     8.037500e+07
std      1.095943e+07
min      6.880000e+07
25%      7.382500e+07
50%      7.915000e+07
75%      8.570000e+07
max      9.440000e+07
Name: Egypt, dtype: float64


fig,ax = plt.subplots(figsize=(15,10),dpi=70)
splot = sns.barplot(x=ye2,y=comparing.Russia,data=df_Population,ci=95,ax=ax,palette = "mako")
for p in splot.patches:
    if p.get_height() > 0: # -- > i used if statments bcz when i try to use annotators , there is some countries that have negative values , so the position of the value gets in the bar.. 
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, 9), 
                       textcoords = 'offset points')
    if p.get_height() < 0 : # --> i had to seperate the positive values from the negative values and give each ot them a different axis...
        splot.annotate(format(p.get_height(), ".1f"), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, -11), # ---> Negative y axis since its a negative value ...
                       textcoords = 'offset points')

for patch in ax.patches :
        current_width = patch.get_width()
        diff = current_width - 0.1

        # we change the bar width
        patch.set_width(.1)

        # we recenter the bar
        patch.set_x(patch.get_x() + diff * .5)
        
        
title_size = 18
ax.set_title("Russia Population" ,fontsize=title_size)
ax.set_ylabel("Ratio",fontsize=title_size)
ax.tick_params(labelsize=16,length=0)
plt.tight_layout()


Russia_Population = 14600000 - 14500000
Russia_Population

100000


comparing.Russia.describe()

count    4.000000e+00
mean     1.445000e+08
std      1.290994e+06
min      1.430000e+08
25%      1.437500e+08
50%      1.445000e+08
75%      1.452500e+08
max      1.460000e+08
Name: Russia, dtype: float64


fig,ax = plt.subplots(figsize=(15,10),dpi=70)
splot = sns.barplot(x=ye2,y=comparing.China,data=df_Population,ci=95,ax=ax,palette = "cubehelix")
for p in splot.patches:
    if p.get_height() > 0: # -- > i used if statments bcz when i try to use annotators , there is some countries that have negative values , so the position of the value gets in the bar.. 
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, 9), 
                       textcoords = 'offset points')
    if p.get_height() < 0 : # --> i had to seperate the positive values from the negative values and give each ot them a different axis...
        splot.annotate(format(p.get_height(), ".1f"), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, -11), # ---> Negative y axis since its a negative value ...
                       textcoords = 'offset points')

for patch in ax.patches :
        current_width = patch.get_width()
        diff = current_width - 0.1

        # we change the bar width
        patch.set_width(.1)

        # we recenter the bar
        patch.set_x(patch.get_x() + diff * .5)
        
        
title_size = 18
ax.set_title("China Population" ,fontsize=title_size)
ax.set_ylabel("Ratio",fontsize=title_size)
ax.tick_params(labelsize=16,length=0)
plt.tight_layout()


China_Population = 1410000000 - 1290000000
China_Population

120000000


comparing.China.describe()

count    4.000000e+00
mean     1.350000e+09
std      5.163978e+07
min      1.290000e+09
25%      1.320000e+09
50%      1.350000e+09
75%      1.380000e+09
max      1.410000e+09
Name: China, dtype: float64


fig,ax = plt.subplots(figsize=(15,10),dpi=70)
splot = sns.barplot(x=ye2,y=comparing.Japan,data=df_Population,ci=95,ax=ax,palette = "rocket_r")
for p in splot.patches:
    if p.get_height() > 0: # -- > i used if statments bcz when i try to use annotators , there is some countries that have negative values , so the position of the value gets in the bar.. 
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, 9), 
                       textcoords = 'offset points')
    if p.get_height() < 0 : # --> i had to seperate the positive values from the negative values and give each ot them a different axis...
        splot.annotate(format(p.get_height(), ".1f"), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, -11), # ---> Negative y axis since its a negative value ...
                       textcoords = 'offset points')

for patch in ax.patches :
        current_width = patch.get_width()
        diff = current_width - 0.1

        # we change the bar width
        patch.set_width(.1)

        # we recenter the bar
        patch.set_x(patch.get_x() + diff * .5)
        
        
title_size = 18
ax.set_title("Japan Population" ,fontsize=title_size)
ax.set_ylabel("Ratio",fontsize=title_size)
ax.tick_params(labelsize=16,length=0)
plt.tight_layout()


Japan_Population = 129000000 - 128000000
Japan_Population

1000000


comparing.Japan.describe()

count            4.0
mean     128250000.0
std         500000.0
min      128000000.0
25%      128000000.0
50%      128000000.0
75%      128250000.0
max      129000000.0
Name: Japan, dtype: float64


fig,ax = plt.subplots(figsize=(15,10),dpi=70)
splot = sns.barplot(x=ye2,y=comparing["United States"],data=df_Population,ci=95,ax=ax,palette = "RdYlGn")
for p in splot.patches:
    if p.get_height() > 0: # -- > i used if statments bcz when i try to use annotators , there is some countries that have negative values , so the position of the value gets in the bar.. 
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, 9), 
                       textcoords = 'offset points')
    if p.get_height() < 0 : # --> i had to seperate the positive values from the negative values and give each ot them a different axis...
        splot.annotate(format(p.get_height(), ".1f"), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, -11), # ---> Negative y axis since its a negative value ...
                       textcoords = 'offset points')

for patch in ax.patches :
        current_width = patch.get_width()
        diff = current_width - 0.1

        # we change the bar width
        patch.set_width(.1)

        # we recenter the bar
        patch.set_x(patch.get_x() + diff * .5)
        
        
title_size = 18
ax.set_title("United States Population" ,fontsize=title_size)
ax.set_ylabel("Ratio",fontsize=title_size)
ax.tick_params(labelsize=16,length=0)
plt.tight_layout()


USA_Population = 323000000 - 282000000
USA_Population

41000000


comparing["United States"].describe()

count    4.000000e+00
mean     3.022500e+08
std      1.768945e+07
min      2.820000e+08
25%      2.917500e+08
50%      3.020000e+08
75%      3.125000e+08
max      3.230000e+08
Name: United States, dtype: float64


comparing_Afghanistan_Population.values

array([20800000., 25700000., 29200000., 35400000.])


fig,ax = plt.subplots(figsize=(15,10),dpi=70)
comparing_Afghanistan_Population = Comparing(df_Population.Afghanistan)
splot = sns.barplot(x=ye2,y=comparing_Afghanistan_Population,data=df_Population,ci=95,ax=ax,palette = "magma")
for p in splot.patches:
    if p.get_height() > 0: # -- > i used if statments bcz when i try to use annotators , there is some countries that have negative values , so the position of the value gets in the bar.. 
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, 9), 
                       textcoords = 'offset points')
    if p.get_height() < 0 : # --> i had to seperate the positive values from the negative values and give each ot them a different axis...
        splot.annotate(format(p.get_height(), ".1f"), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, -11), # ---> Negative y axis since its a negative value ...
                       textcoords = 'offset points')

for patch in ax.patches :
        current_width = patch.get_width()
        diff = current_width - 0.1

        # we change the bar width
        patch.set_width(.1)

        # we recenter the bar
        patch.set_x(patch.get_x() + diff * .5)
        
title_size = 18
ax.set_title("Afghanistan Population" ,fontsize=title_size)
ax.set_ylabel("Ratio",fontsize=title_size)
ax.tick_params(labelsize=16,length=0)
plt.tight_layout()


Afghanistan_Population = 354000000 - 208000000
Afghanistan_Population

146000000


comparing.Afghanistan.describe()

count    4.000000e+00
mean     2.777500e+07
std      6.140779e+06
min      2.080000e+07
25%      2.447500e+07
50%      2.745000e+07
75%      3.075000e+07
max      3.540000e+07
Name: Afghanistan, dtype: float64


population = {}
for country in df_Population.columns:
    population[df_Population[country].mean()] = country


print("The country with the most Population Rate is {} with an average of --> {} ....".format(population[max(population.keys())],max(population.keys())))

The country with the most Population Rate is China with an average of --> 1308888888.8888888 ....


print("The country with the most Population Rate is {} with an average of --> {} ....".format(population[min(population.keys())],min(population.keys())))

The country with the most Population Rate is Holy See with an average of --> 785.5925925925926 ....


plt.style.use('seaborn-dark')
comparing_Egypt_GDP = Comparing(df_GDP.Egypt)
comparing_Russia_GDP = Comparing(df_GDP.Russia)
comparing_China_GDP = Comparing(df_GDP.China)
comparing_Afghanistan_GDP = Comparing(df_GDP.Afghanistan)
comparing_Japan_GDP = Comparing(df_GDP.Japan)
comparing_USA_GDP = Comparing(df_GDP["United States"])

fig, ax = plt.subplots(figsize = (15,10),dpi=65)

a = ax.plot(comparing_Egypt_GDP.index, comparing_Egypt_GDP.values, 'o-',color='#7f6d5f', label='Egypt')
ax.plot(comparing_China_GDP.index, comparing_China_GDP.values, 'o-', color='#557f2d', label='China')
ax.plot(comparing_Russia_GDP.index, comparing_Russia_GDP.values,'D-',color='#2d7f5e',label='Russia')
ax.plot(comparing_Japan_GDP.index, comparing_Japan_GDP.values, 'o-',color='#120603',label='Japan')
ax.plot(comparing_Afghanistan_GDP.index, comparing_Afghanistan_GDP.values, 'o-',color='#c912c6',label='Afghanistan')
ax.plot(comparing_USA_GDP.index, comparing_USA_GDP.values, 'o-',color='#DE5833',label='United States')
ax.set_title('GDP Growth for group of countries')
ax.set_xlabel('Years')
ax.set_ylabel('Ratio')
plt.legend()
plt.grid(axis = 'x', linestyle=':', alpha=1,color = 'black')
plt.grid(axis = 'y', linestyle=':', alpha=1,color = 'black')
plt.tight_layout()
plt.show()


fig,ax = plt.subplots(figsize=(10,7),dpi=65)
splot = sns.barplot(x=comparing_Egypt_GDP.index,y=comparing_Egypt_GDP.values,data=df_GDP,ci=95,ax=ax,palette = "viridis")
for p in splot.patches:
    if p.get_height() > 0: # -- > i used if statments bcz when i try to use annotators , there is some countries that have negative values , so the position of the value gets in the bar.. 
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, 9), 
                       textcoords = 'offset points')
    if p.get_height() < 0 : # --> i had to seperate the positive values from the negative values and give each ot them a different axis...
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, -11), # ---> Negative y axis since its a negative value ...
                       textcoords = 'offset points')
title_size = 18
ax.set_title("Egypt GDP Growth Rate" ,fontsize=title_size)
ax.set_ylabel("Ratio",fontsize=title_size)
ax.tick_params(labelsize=16,length=0)
plt.tight_layout()


df_GDP.Egypt.describe()

count    27.000000
mean      4.357928
std       1.650896
min       1.125405
25%       3.054683
50%       4.471744
75%       5.533926
max       7.156284
Name: Egypt, dtype: float64


fig,ax = plt.subplots(figsize=(10,7),dpi=65)
splot = sns.barplot(x=comparing_Russia_GDP.index,y=comparing_Russia_GDP.values,data=df_GDP,ci=95,ax=ax,palette = "mako")
for p in splot.patches:
    if p.get_height() > 0: # -- > i used if statments bcz when i try to use annotators , there is some countries that have negative values , so the position of the value gets in the bar.. 
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, 9), 
                       textcoords = 'offset points')
    if p.get_height() < 0 : # --> i had to seperate the positive values from the negative values and give each ot them a different axis...
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, -11), # ---> Negative y axis since its a negative value ...
                       textcoords = 'offset points')
    
ax.set_title("Russia GDP Growth Rate",fontsize=title_size)
ax.set_ylabel("Ratio",fontsize=title_size)
ax.tick_params(labelsize=16,length=0)
plt.grid(axis = 'y', linestyle=':', alpha=1,color = 'black')
plt.tight_layout()


df_GDP.Russia.describe()

count    27.000000
mean      0.708213
std       6.655096
min     -14.531074
25%      -3.949299
50%       1.755422
75%       5.799942
max      10.000067
Name: Russia, dtype: float64


fig,ax = plt.subplots(figsize=(10,7),dpi=65)
splot = sns.barplot(x=comparing_USA_GDP.index,y=comparing_USA_GDP.values,data=df_GDP,ci=95,ax=ax,palette = "RdYlGn")
for p in splot.patches:
    if p.get_height() > 0:
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, 9), 
                       textcoords = 'offset points')
    if p.get_height() < 0 :
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, -11), 
                       textcoords = 'offset points')
    
ax.set_title("United States GDP Growth Rate",fontsize=title_size)
ax.set_ylabel("Ratio",fontsize=title_size)
ax.tick_params(labelsize=16,length=0)
plt.tight_layout()


df_GDP["United States"].describe()

count    27.000000
mean      2.473026
std       1.620977
min      -2.536757
25%       1.791888
50%       2.684217
75%       3.647503
max       4.753236
Name: United States, dtype: float64


fig,ax = plt.subplots(figsize=(10,7),dpi=65)
splot = sns.barplot(x=comparing_Afghanistan_GDP.index,y=comparing_Afghanistan_GDP.values,data=df_GDP,ci=95,ax=ax,palette = "viridis")
for p in splot.patches:
    if p.get_height() > 0:
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, 9), 
                       textcoords = 'offset points')
    if p.get_height() < 0 :
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, -11), 
                       textcoords = 'offset points')
    
ax.set_title("Afghanistan GDP Growth Rate",fontsize=title_size)
ax.set_ylabel("Ratio",fontsize=title_size)
ax.tick_params(labelsize=16,length=0)
plt.tight_layout()


df_GDP.Afghanistan.describe()

count    27.000000
mean      3.909383
std       5.888010
min       0.000000
25%       0.000000
50%       0.426355
75%       5.479074
max      21.390528
Name: Afghanistan, dtype: float64


fig,ax = plt.subplots(figsize=(10,7),dpi=65)
splot = sns.barplot(x=comparing_China_GDP.index,y=comparing_China_GDP.values,data=df_GDP,ci=95,ax=ax,palette = "cubehelix")
for p in splot.patches:
    if p.get_height() > 0:
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, 9), 
                       textcoords = 'offset points')
    if p.get_height() < 0 :
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, -11), 
                       textcoords = 'offset points')
    
ax.set_title("China GDP Growth Rate",fontsize=title_size)
ax.set_ylabel("Ratio",fontsize=title_size)
ax.tick_params(labelsize=16,length=0)
plt.tight_layout()


df_GDP.China.describe()

count    27.000000
mean      9.651421
std       2.457343
min       3.920251
25%       7.854844
50%       9.398726
75%      10.794913
max      14.230861
Name: China, dtype: float64


fig,ax = plt.subplots(figsize=(10,7),dpi=65)
splot = sns.barplot(x=comparing_Japan_GDP.index,y=comparing_Japan_GDP.values,data=df_GDP,ci=95,ax=ax,palette = "rocket_r")
for p in splot.patches:
    if p.get_height() > 0:
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, 9), 
                       textcoords = 'offset points')
    if p.get_height() < 0 :
        splot.annotate(format(p.get_height(), '.1f'), 
                       (p.get_x() + p.get_width() / 2., p.get_height()), 
                       ha = 'center', va = 'center', 
                       size=15,
                       xytext = (0, -11), 
                       textcoords = 'offset points')
ax.set_title("Japan GDP Growth Rate",fontsize=title_size)
ax.set_ylabel("Ratio",fontsize=title_size)
ax.tick_params(labelsize=16,length=0)
plt.tight_layout()


df_GDP.Japan.describe()

count    27.000000
mean      1.094228
std       2.025525
min      -5.693236
25%       0.169084
50%       1.372350
75%       2.095608
max       4.892713
Name: Japan, dtype: float64


economy = {}
for country in df_GDP.columns[1:]:
    economy[df_GDP[country].mean()] = country


print("The country with the most economy Growth is {} with an average of --> {} ....".format(economy[max(economy.keys())],max(economy.keys())))

The country with the most economy Growth is Equatorial Guinea with an average of --> 19.974110408814813 ....


print("The country with the least Growth is {} with an average of --> {} ....".format(economy[min(economy.keys())],min(economy.keys())))

The country with the least Growth is Ukraine with an average of --> -1.8408876753703698 ....


plt.figure(figsize=(15,10),dpi= 65)
comparing_Egypt_Income = Comparing(df_Income.Egypt)
comparing_Russia_Income = Comparing(df_Income.Russia)
comparing_China_Income = Comparing(df_Income.China)
comparing_Afghanistan_Income = Comparing(df_Income.Afghanistan)
comparing_Japan_Income = Comparing(df_Income.Japan)
comparing_USA_Income = Comparing(df_Income["United States"])

plt.step(comparing_Egypt_Income.index, comparing_Egypt_Income.values, label='Egypt')
plt.plot(comparing_Egypt_Income.index, comparing_Egypt_Income.values, 'o--', color='blue', alpha=0.3)
for x, y in zip(comparing_Egypt_Income.index, comparing_Egypt_Income.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#1E5A6A', 'size':14})

plt.step(comparing_Russia_Income.index, comparing_Russia_Income.values, label='Russia')
plt.plot(comparing_Russia_Income.index, comparing_Russia_Income.values, 'o--', color='#a1a30b', alpha=0.3)
for x, y in zip(comparing_Russia_Income.index, comparing_Russia_Income.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#7d870e', 'size':14})
    
plt.step(comparing_China_Income.index, comparing_China_Income.values, label='China')
plt.plot(comparing_China_Income.index, comparing_China_Income.values, 'o--', color='green', alpha=0.3)
for x, y in zip(comparing_China_Income.index, comparing_China_Income.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#18540C', 'size':14})
    
plt.step(comparing_Japan_Income.index, comparing_Japan_Income.values, label='Japan')
plt.plot(comparing_Japan_Income.index, comparing_Japan_Income.values, 'o--', color='red', alpha=0.3)
for x, y in zip(comparing_Japan_Income.index, comparing_Japan_Income.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#940025', 'size':14})
    
plt.step(comparing_Afghanistan_Income.index, comparing_Afghanistan_Income.values, label='Afghanistan')
plt.plot(comparing_Afghanistan_Income.index, comparing_Afghanistan_Income.values, 'o--', color='purple', alpha=0.3)
for x, y in zip(comparing_Afghanistan_Income.index, comparing_Afghanistan_Income.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#96005d', 'size':14})
    
plt.step(comparing_USA_Income.index, comparing_USA_Income.values, label='United States')
plt.plot(comparing_USA_Income.index, comparing_USA_Income.values, 'o--', color='grey', alpha=0.3)
for x, y in zip(comparing_USA_Income.index, comparing_USA_Income.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#474747', 'size':14})
    
plt.title('Countries income Growth..',fontsize=15)
plt.xlabel("Years")
plt.ylabel("Income")
plt.grid(axis = 'y', linestyle=':', alpha=1,color = 'black')
plt.legend(fontsize = 14 , loc = "center left")
plt.tight_layout()
plt.show()


plt.figure(figsize=(15,10),dpi= 65)
plt.step(comparing_Egypt_Income.index, comparing_Egypt_Income.values, label='Egypt')
plt.plot(comparing_Egypt_Income.index, comparing_Egypt_Income.values, 'o--', color='blue', alpha=0.3)
for x, y in zip(comparing_Egypt_Income.index, comparing_Egypt_Income.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#065266', 'size':14})
plt.title('Egypt income Growth..',fontsize=15)
plt.xlabel("Years")
plt.ylabel("Income")
plt.grid(axis = 'x', linestyle='dashdot', alpha=1,color = 'blue')
plt.legend(loc = "upper left", fontsize = 14)
plt.tight_layout()
plt.show()


df_Income.Egypt.describe()

count       27.00000
mean      8309.62963
std       1664.35823
min       6020.00000
25%       6785.00000
50%       8010.00000
75%      10150.00000
max      10800.00000
Name: Egypt, dtype: float64


plt.figure(figsize=(15,10),dpi= 65)
plt.step(comparing_Russia_Income.index, comparing_Russia_Income.values, label='Russia', color = "#7d870e")
plt.plot(comparing_Russia_Income.index, comparing_Russia_Income.values, 'o--', color='#7d870e', alpha=0.3)
for x, y in zip(comparing_Russia_Income.index, comparing_Russia_Income.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#596100', 'size':14})
plt.title('Russia income Growth..',fontsize=15)
plt.xlabel("Years")
plt.ylabel("Income")
plt.grid(axis = 'x', linestyle='dashdot', alpha=1,color = '#596100')
plt.legend(loc = "upper left", fontsize = 14)
plt.tight_layout()
plt.show()


df_Income.Russia.describe()

count       27.000000
mean     19585.185185
std       4987.422928
min      12400.000000
25%      15000.000000
50%      20100.000000
75%      24500.000000
max      26400.000000
Name: Russia, dtype: float64


plt.figure(figsize=(15,10),dpi= 65)
plt.step(comparing_China_Income.index, comparing_China_Income.values, label='China', color = "#18540C")
plt.plot(comparing_China_Income.index, comparing_China_Income.values, 'o--', color='green', alpha=0.3)
for x, y in zip(comparing_China_Income.index, comparing_China_Income.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#18540C', 'size':14})
plt.title('China income Growth..',fontsize=15)
plt.xlabel("Years")
plt.ylabel("Income")
plt.grid(axis = 'x', linestyle='dashdot', alpha=1,color = '#18540C')
plt.legend(loc = "upper left", fontsize = 14)
plt.tight_layout()
plt.show()


df_Income.China.describe()

count       27.000000
mean      5744.814815
std       3778.361845
min       1420.000000
25%       2705.000000
50%       4400.000000
75%       8480.000000
max      13500.000000
Name: China, dtype: float64


plt.figure(figsize=(15,10),dpi= 65)
plt.step(comparing_Japan_Income.index, comparing_Japan_Income.values, label='Japan',color="#940025")
plt.plot(comparing_Japan_Income.index, comparing_Japan_Income.values, 'o--', color='red', alpha=0.3)
for x, y in zip(comparing_Japan_Income.index, comparing_Japan_Income.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#940025', 'size':14})
plt.title('Japan income Growth..',fontsize=15)
plt.xlabel("Years")
plt.ylabel("Income")
plt.grid(axis = 'x', linestyle='dashdot', alpha=1,color = '#940025')
plt.legend(loc = "upper left", fontsize = 14)
plt.tight_layout()
plt.show()


df_Income.Japan.describe()

count       27.000000
mean     36377.777778
std       2249.672341
min      32300.000000
25%      34900.000000
50%      36200.000000
75%      38200.000000
max      40200.000000
Name: Japan, dtype: float64


plt.figure(figsize=(15,10),dpi= 65)
plt.step(comparing_USA_Income.index, comparing_USA_Income.values, label='United States',color="#474747")
plt.plot(comparing_USA_Income.index, comparing_USA_Income.values, 'o--', color='grey', alpha=0.3)
for x, y in zip(comparing_USA_Income.index, comparing_USA_Income.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#474747', 'size':14})
plt.title('United States income Growth..',fontsize=15)
plt.xlabel("Years")
plt.ylabel("Income")
plt.grid(axis = 'x', linestyle='dashdot', alpha=1,color = '#474747')
plt.legend(loc = "upper left", fontsize = 14)
plt.tight_layout()
plt.show()


df_Income["United States"].describe()

count       27.000000
mean     50333.333333
std       6139.218191
min      39900.000000
25%      44850.000000
50%      51600.000000
75%      55350.000000
max      59000.000000
Name: United States, dtype: float64


plt.figure(figsize=(15,10),dpi= 65)
plt.step(comparing_Afghanistan_Income.index, comparing_Afghanistan_Income.values, label='Afghanistan',color = '#96005d')
plt.plot(comparing_Afghanistan_Income.index, comparing_Afghanistan_Income.values, 'o--', color='purple', alpha=0.3)
for x, y in zip(comparing_Afghanistan_Income.index, comparing_Afghanistan_Income.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#96005d', 'size':14})
plt.title('Afghanistan income Growth..',fontsize=15)
plt.xlabel("Years")
plt.ylabel("Income")
plt.grid(axis = 'x', linestyle='dashdot', alpha=1,color = '#96005d')
plt.legend(loc = "upper left", fontsize = 14)
plt.tight_layout()
plt.show()


df_Income.Afghanistan.describe()

count      27.000000
mean     1256.592593
std       573.533331
min       493.000000
25%       677.500000
50%      1200.000000
75%      1835.000000
max      2120.000000
Name: Afghanistan, dtype: float64


Income = {}
for country in df_Income.columns[1:]:
    Income[df_Income[country].mean()] = country


print("The country with the most Income Rate is {} with an average of --> ${}  ....".format(Income[max(Income.keys())],max(Income.keys())))

The country with the most Income Rate is Luxembourg with an average of --> $95451.85185185185  ....


print("The country with the least Income Rate is {} with an average of --> ${} ....".format(Income[min(Income.keys())],min(Income.keys())))

The country with the least Income Rate is Somalia with an average of --> $732.1481481481482 ....


plt.figure(figsize=(15,10),dpi= 65)
comparing_Egypt_Unemployment = Comparing(df_Unemployment.Egypt)
comparing_Russia_Unemployment = Comparing(df_Unemployment.Russia)
comparing_China_Unemployment = Comparing(df_Unemployment.China)
comparing_Afghanistan_Unemployment = Comparing(df_Unemployment.Afghanistan)
comparing_Japan_Unemployment = Comparing(df_Unemployment.Japan)
comparing_USA_Unemployment = Comparing(df_Unemployment["United States"])
plt.plot(comparing_Egypt_Unemployment.index,comparing_Egypt_Unemployment.values,'->',markerfacecolor = "yellow", label='Egypt')
for x, y in zip(comparing_Egypt_Unemployment.index, comparing_Egypt_Unemployment.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#065266', 'size':14})

plt.plot(comparing_Russia_Unemployment.index,comparing_Russia_Unemployment.values,'-*',markerfacecolor = "green" ,label='Russia')
for x, y in zip(comparing_Russia_Unemployment.index, comparing_Russia_Unemployment.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#596100', 'size':14})

plt.plot(comparing_China_Unemployment.index,comparing_China_Unemployment.values,'-o',markerfacecolor = "red",  label='China')
for x, y in zip(comparing_China_Unemployment.index, comparing_China_Unemployment.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#18540C', 'size':14})

plt.plot(comparing_Afghanistan_Unemployment.index,comparing_Afghanistan_Unemployment.values,'-^',markerfacecolor = "blue" , label='Afghanistan')
for x, y in zip(comparing_Afghanistan_Unemployment.index, comparing_Afghanistan_Unemployment.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#96005d', 'size':14})

plt.plot(comparing_USA_Unemployment.index,comparing_USA_Unemployment.values,'-x',markerfacecolor = "purple" , label='United States')
for x, y in zip(comparing_USA_Unemployment.index, comparing_USA_Unemployment.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#474747', 'size':14})

plt.plot(comparing_Japan_Unemployment.index,comparing_Japan_Unemployment.values,'-2',markerfacecolor = "gray",  label='Japan')
for x, y in zip(comparing_Japan_Unemployment.index, comparing_Japan_Unemployment.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#940025', 'size':14})

plt.title('Countries Unemployment rate..',fontsize=15)
plt.xlabel("Years")
plt.ylabel("Unemployment rate")
plt.grid(axis = 'x', linestyle='dashdot', alpha=1,color = '#96005d')
plt.legend(fontsize=14)
plt.tight_layout()


plt.figure(figsize=(15,10),dpi= 65)
plt.plot(comparing_Egypt_Unemployment.index, comparing_Egypt_Unemployment.values,'->',markerfacecolor = "yellow", label='Egypt')
for x, y in zip(comparing_Egypt_Unemployment.index, comparing_Egypt_Unemployment.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#065266', 'size':14})

plt.title('Egypt Unemployment Rate..',fontsize=15)
plt.xlabel("Years")
plt.ylabel("Unemployment rate")
plt.legend(fontsize=14,loc= "upper left")
plt.grid(axis = 'x', linestyle='dashdot', alpha=1,color = 'blue')
plt.tight_layout()
plt.show()


df_Unemployment.Egypt.describe()

count    27.000000
mean      9.894074
std       2.564502
min       0.000000
25%       8.860000
50%      10.010000
75%      11.120000
max      13.150000
Name: Egypt, dtype: float64


plt.figure(figsize=(15,10),dpi= 65)
plt.plot(comparing_Russia_Unemployment.index, comparing_Russia_Unemployment.values,'-*',color = "green",markerfacecolor = "yellow" ,label='Russia')
for x, y in zip(comparing_Russia_Unemployment.index, comparing_Russia_Unemployment.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#065266', 'size':14})

plt.title('Russia Unemployment Rate..',fontsize=15)
plt.xlabel("Years")
plt.ylabel("Unemployment rate")
plt.legend(fontsize=14,loc= "upper right")
plt.grid(axis = 'x', linestyle='dashdot', alpha=1,color = 'green')
plt.tight_layout()
plt.show()


df_Unemployment["Russia"].describe()

count    27.000000
mean      7.445370
std       2.770113
min       0.000000
25%       5.565000
50%       7.120000
75%       8.640000
max      13.260000
Name: Russia, dtype: float64


plt.figure(figsize=(15,10),dpi= 65)
plt.plot(comparing_China_Unemployment.index, comparing_China_Unemployment.values,'-o',color = "red",markerfacecolor = "yellow",  label='China')
for x, y in zip(comparing_China_Unemployment.index, comparing_China_Unemployment.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#18540C', 'size':14})

plt.title('China Unemployment Rate..',fontsize=15)
plt.xlabel("Years")
plt.ylabel("Unemployment rate")
plt.legend(fontsize=14,loc= "upper left")
plt.grid(axis = 'x', linestyle='dashdot', alpha=1,color = 'red')
plt.tight_layout()
plt.show()


df_Unemployment["China"].describe()

count    27.000000
mean      3.746667
std       1.091696
min       0.000000
25%       3.175000
50%       4.350000
75%       4.560000
max       4.720000
Name: China, dtype: float64


plt.figure(figsize=(15,10),dpi= 65)
plt.plot(comparing_Afghanistan_Unemployment.index, comparing_Afghanistan_Unemployment.values,'-^',color = "black",markerfacecolor = "red" , label='Afghanistan')
for x, y in zip(comparing_Afghanistan_Unemployment.index, comparing_Afghanistan_Unemployment.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#96005d', 'size':14})

plt.title('Afghanistan Unemployment Rate..',fontsize=15)
plt.xlabel("Years")
plt.ylabel("Unemployment rate")
plt.legend(fontsize=14,loc= "upper left")
plt.grid(axis = 'x', linestyle='dashdot', alpha=1,color = 'black')
plt.tight_layout()
plt.show()


df_Unemployment["Afghanistan"].describe()

count    27.000000
mean     10.623815
std       2.133948
min       0.000000
25%      10.807500
50%      11.093000
75%      11.186000
max      11.352000
Name: Afghanistan, dtype: float64


plt.figure(figsize=(15,10),dpi= 65)
plt.plot(comparing_USA_Unemployment.index, comparing_USA_Unemployment.values,'-x',color = "purple",markerfacecolor = "purple" , label='United States')
for x, y in zip(comparing_USA_Unemployment.index, comparing_USA_Unemployment.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#474747', 'size':14})
    
plt.title('United States Unemployment Rate..',fontsize=15)
plt.xlabel("Years")
plt.ylabel("Unemployment rate")
plt.legend(fontsize=14,loc= "upper left")
plt.grid(axis = 'x', linestyle='dashdot', alpha=1,color = 'purple')
plt.tight_layout()
plt.show()


df_Unemployment["United States"].describe()

count    27.000000
mean      5.846667
std       1.930992
min       0.000000
25%       4.800000
50%       5.650000
75%       6.850000
max       9.630000
Name: United States, dtype: float64


plt.figure(figsize=(15,10),dpi= 65)
plt.plot(comparing_Japan_Unemployment.index, comparing_Japan_Unemployment.values,'-2',color = "#0e786c",markerfacecolor = "gray",  label='Japan')
for x, y in zip(comparing_Japan_Unemployment.index, comparing_Japan_Unemployment.values):
    t = plt.text(x, y,round(y, 2), verticalalignment='top' if y < 0 else 'bottom', 
                 fontdict={'color':'#940025', 'size':14})
plt.title('Japan Unemployment Rate..',fontsize=15)
plt.xlabel("Years")
plt.ylabel("Unemployment rate")
plt.legend(fontsize=14,loc= "upper left")
plt.grid(axis = 'x', linestyle='dashdot', alpha=1,color = '#0e786c')
plt.tight_layout()
plt.show()


df_Unemployment["Japan"].describe()

count    27.000000
mean      3.827778
std       1.198834
min       0.000000
25%       3.300000
50%       4.000000
75%       4.715000
max       5.390000
Name: Japan, dtype: float64


Unemployment = {}
for country in df_Unemployment.columns:
    Unemployment[df_Unemployment[country].mean()] = country


print("The country with the most Unemployment rate is {} with an average of --> {} ....".format(Unemployment[max(Unemployment.keys())],max(Unemployment.keys())))

The country with the most Unemployment rate is North Macedonia with an average of --> 30.729629517037043 ....


print("The country with the least Unemployment Rate is {} with an average of --> {} ....".format(Unemployment[min(Unemployment.keys())],min(Unemployment.keys())))

The country with the least Unemployment Rate is Kosovo with an average of --> 0.0 ....


macdonia_Unemployment = df_Unemployment["North Macedonia"]


macdonia_Unemployment.mean()

30.729629517037043


macdonia_Murder = murder_ratio["North Macedonia"]


macdonia_Murder.mean()

103.1156044669988


plt.figure(figsize=(15,10), dpi= 65)
sns.histplot(macdonia_Murder.values, color="dodgerblue", label="Murder", kde=True,stat="density", linewidth=0.5)
sns.histplot(macdonia_Unemployment.values, color="orange", label="Unemployment", kde=True,stat="density", linewidth=0.5)
# Decoration
plt.title('North Macedonia', fontsize=15)
plt.ylabel("")
plt.legend()
plt.show()


income_Luxembourg = df_Income.Luxembourg


income_Luxembourg.mean()

95451.85185185185


murder_Luxembourg = df_Murder.Luxembourg


murder_Luxembourg.mean()

6.7059259259259285


# Plot
fig, ax = plt.subplots(figsize=(15,10), dpi= 65)    
sns.stripplot(x=murder_Luxembourg, y= income_Luxembourg, jitter=0.25, size=8, ax=ax, linewidth=.5)
# Decorations
plt.title('Luxembourg', fontsize=14)
plt.ylabel("Income", fontsize=14)
plt.xlabel("Murder Rate", fontsize=14)
plt.grid(axis = 'y', linestyle='dashdot', alpha=0.2,color = '#0e786c')
plt.show()


Population_China = df_Population.China


Population_China.mean()

1308888888.8888888


Unemployment_China = df_Unemployment.China


Unemployment_China.mean()

3.746666669925926


plt.figure(figsize=(14,16), dpi= 65)
plt.scatter(Unemployment_China, Population_China, s=450, alpha=.6)
for x, y, tex in zip(Unemployment_China, Population_China, Unemployment_China):
    t = plt.text(x, y, round(tex, 1), horizontalalignment='center', 
                 verticalalignment='center', fontdict={'color':'white'})

# Decorations
# Lighten borders
plt.gca().spines["top"].set_alpha(.3)
plt.gca().spines["bottom"].set_alpha(.3)
plt.gca().spines["right"].set_alpha(.3)
plt.gca().spines["left"].set_alpha(.3)

# plt.yticks(Unemployment_China.index)
plt.title('China', fontdict={'size':16})
plt.xlabel('Unemployment Rate',fontsize=16)
plt.ylabel("Population",fontsize=16)
plt.grid(linestyle='--', alpha=0.5)
# plt.xlim(-2.5, 2.5)
plt.show()


Murder_Brazil = df_Murder.Brazil


Murder_Brazil.mean()

52555.555555555555


GDP_Brazil = df_GDP.Brazil


GDP_Brazil.mean()

2.281282081444444


# Draw Plot
plt.figure(figsize=(15,10), dpi= 65)
plt.scatter(Murder_Brazil, GDP_Brazil, c=Murder_Brazil, cmap='plasma')
plt.colorbar()
plt.title('Brazil',fontsize=16)
plt.xlabel('Murder Rate',fontsize=16)
plt.ylabel('GDP Rate',fontsize=16)
plt.grid(alpha=0.5)
plt.show()

	Country	1960	1961	1962	1963	1964	1965	1966	1967	1968	...	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
0	Aruba	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	3.446055	-1.369863	4.198232	0.300000	5.700001	2.100000	1.999999	NaN	NaN	NaN
1	Africa Eastern and Southern	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	4.014183	1.972652	4.308370	3.986754	2.925591	2.019391	2.542298	2.475272	2.077898	-2.939186
2	Afghanistan	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	0.426355	12.752287	5.600745	2.724543	1.451315	2.260314	2.647003	1.189228	3.911603	-2.351101
3	Africa Western and Central	NaN	1.848719	3.770212	7.272501	5.396356	4.049794	-1.787094	-9.546521	1.465741	...	4.848351	5.142964	6.104241	5.927350	2.745937	0.127595	2.318042	2.952230	3.190336	-0.884981
4	Angola	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	3.471976	8.542188	4.954545	4.822628	0.943572	-2.580050	-0.147213	-2.003630	-0.624644	-5.399987

	country	1990	1991	1992	1993	1994	1995	1996	1997	1998	...	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
0	Afghanistan	2070	2200	2380	2600	2830	3020	3160	3270	3350	...	4910	4960	4990	4940	5020	5190	5560	5820	6060	6270
1	Angola	527	532	543	569	598	608	582	582	667	...	904	933	958	978	990	1010	1030	1050	1080	1090
2	Albania	160	182	201	221	239	267	295	327	338	...	82.7	77.5	67.5	68.4	68.5	68.5	68.7	68.9	69.2	69.5
3	Andorra	0.48	0.51	0.54	0.55	0.55	0.53	0.51	0.5	0.49	...	0.52	0.52	0.53	0.54	0.54	0.54	0.55	0.55	0.55	0.55
4	United Arab Emirates	31.4	31.6	32.2	33.2	34.4	35.8	37.1	38.5	40.2	...	81.7	94.9	109	122	129	132	133	133	133	132

	country	1800	1801	1802	1803	1804	1805	1806	1807	1808	...	2091	2092	2093	2094	2095	2096	2097	2098	2099	2100
0	Afghanistan	3.28M	3.28M	3.28M	3.28M	3.28M	3.28M	3.28M	3.28M	3.28M	...	76.6M	76.4M	76.3M	76.1M	76M	75.8M	75.6M	75.4M	75.2M	74.9M
1	Angola	1.57M	1.57M	1.57M	1.57M	1.57M	1.57M	1.57M	1.57M	1.57M	...	168M	170M	172M	175M	177M	179M	182M	184M	186M	188M
2	Albania	400k	402k	404k	405k	407k	409k	411k	413k	414k	...	1.33M	1.3M	1.27M	1.25M	1.22M	1.19M	1.17M	1.14M	1.11M	1.09M
3	Andorra	2650	2650	2650	2650	2650	2650	2650	2650	2650	...	63k	62.9k	62.9k	62.8k	62.7k	62.7k	62.6k	62.5k	62.5k	62.4k
4	United Arab Emirates	40.2k	40.2k	40.2k	40.2k	40.2k	40.2k	40.2k	40.2k	40.2k	...	12.3M	12.4M	12.5M	12.5M	12.6M	12.7M	12.7M	12.8M	12.8M	12.9M

	country	1800	1801	1802	1803	1804	1805	1806	1807	1808	...	2041	2042	2043	2044	2045	2046	2047	2048	2049	2050
0	Afghanistan	674	674	674	674	674	674	674	674	674	...	2880	2940	3000	3070	3130	3200	3270	3340	3410	3480
1	Angola	691	693	697	700	702	705	709	712	716	...	8040	8220	8390	8570	8750	8940	9120	9320	9520	9720
2	Albania	746	746	746	746	746	747	747	747	747	...	24.5k	25k	25.5k	26.1k	26.6k	27.2k	27.8k	28.3k	28.9k	29.6k
3	Andorra	1340	1340	1340	1350	1350	1350	1350	1360	1360	...	108k	111k	113k	116k	118k	121k	123k	126k	128k	131k
4	United Arab Emirates	1120	1120	1120	1130	1130	1140	1140	1150	1150	...	74.5k	76.1k	77.7k	79.3k	81k	82.7k	84.5k	86.3k	88.1k	90k

	Country	1960	1961	1962	1963	1964	1965	1966	1967	1968	...	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
0	Aruba	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	Africa Eastern and Southern	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	6.748081	6.562179	6.445456	6.405195	6.490041	6.610205	6.714955	6.731163	6.914353	7.563187
2	Afghanistan	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	11.054000	11.341000	11.193000	11.142000	11.127000	11.158000	11.180000	11.152000	11.217000	11.710000
3	Africa Western and Central	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	4.548376	4.637602	4.410216	4.688088	4.626737	5.567017	6.019505	6.041092	6.063362	6.774914
4	Angola	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	7.360000	7.347000	7.366000	7.372000	7.392000	7.412000	7.408000	7.421000	7.421000	8.333000

	Country	1990	1991	1992	1993	1994	1995	1996	1997	1998	...	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
0	Aruba	3.961402	7.962872	5.882353	7.307692	8.203903	2.547144	1.185788	7.046874	1.991986	...	1.800226	-0.090708	-10.519749	-3.685029	3.446055	-1.369863	4.198232	0.300000	5.700001	2.100000
1	Africa Eastern and Southern	0.050826	-0.095421	-2.343192	-1.089417	2.051914	4.409975	5.570030	3.425412	1.789507	...	6.857304	4.572539	0.946811	5.152336	4.014183	1.972652	4.308370	3.986754	2.925591	2.019391
2	Afghanistan	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	13.826320	3.924984	21.390528	14.362441	0.426355	12.752287	5.600745	2.724543	1.451315	2.260314
3	Africa Western and Central	6.562921	1.121069	2.693959	-1.160468	-0.299641	1.927028	4.629048	4.234699	3.506516	...	5.530987	6.279223	6.274463	6.957010	4.848351	5.142964	6.104241	5.927350	2.745937	0.127595
4	Angola	-3.450099	0.991359	-5.838281	-23.983417	1.339363	15.000000	13.544370	7.274277	4.691146	...	14.010018	11.166138	0.858713	4.403933	3.471976	8.542188	4.954545	4.822628	0.943572	-2.580050
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
261	Kosovo	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	5.034884	4.939924	6.319886	1.712195	5.340908	3.348804	5.916270	5.571757
262	Yemen, Rep.	0.000000	6.293494	8.207598	4.001966	6.721949	5.669371	4.634967	5.231112	6.006695	...	3.338428	3.647569	3.866230	7.702307	-12.714823	2.392886	4.823415	-0.188574	-27.994546	-9.375124
263	South Africa	-0.317786	-1.018220	-2.137057	1.233520	3.200000	3.100000	4.300000	2.600000	0.500000	...	5.360474	3.191044	-1.538089	3.039733	3.168556	2.396232	2.485468	1.413826	1.321862	0.664552
264	Zambia	-0.481072	-0.036133	-1.730922	6.797274	-8.625442	2.897669	6.218546	3.814007	-0.385746	...	8.352436	7.773896	9.220348	10.298223	5.564602	7.597593	5.057232	4.697992	2.920375	3.776679
265	Zimbabwe	6.988553	5.531782	-9.015570	1.051459	9.235199	0.158026	10.360697	2.680594	2.885212	...	-3.653327	-17.668946	12.019560	19.675323	14.193913	16.665429	1.989493	2.376929	1.779873	0.755869

	country	1990	1991	1992	1993	1994	1995	1996	1997	1998	...	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
0	Afghanistan	12.4M	13.3M	14.5M	15.8M	17.1M	18.1M	18.9M	19.4M	19.7M	...	27.1M	27.7M	28.4M	29.2M	30.1M	31.2M	32.3M	33.4M	34.4M	35.4M
1	Angola	11.8M	12.2M	12.7M	13.1M	13.5M	13.9M	14.4M	14.9M	15.4M	...	20.9M	21.7M	22.5M	23.4M	24.2M	25.1M	26M	26.9M	27.9M	28.8M
2	Albania	3.29M	3.28M	3.25M	3.2M	3.15M	3.11M	3.1M	3.1M	3.11M	...	3.03M	3M	2.97M	2.95M	2.93M	2.91M	2.9M	2.9M	2.89M	2.89M
3	Andorra	54.5k	56.7k	58.9k	61k	62.7k	63.9k	64.4k	64.3k	64.1k	...	82.7k	83.9k	84.5k	84.5k	83.7k	82.4k	80.8k	79.2k	78k	77.3k
4	United Arab Emirates	1.83M	1.94M	2.05M	2.17M	2.29M	2.42M	2.54M	2.67M	2.81M	...	6.17M	7.09M	7.92M	8.55M	8.95M	9.14M	9.2M	9.21M	9.26M	9.36M
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
192	Samoa	163k	164k	165k	167k	169k	170k	171k	172k	173k	...	182k	183k	185k	186k	187k	189k	191k	192k	194k	195k
193	Yemen	11.7M	12.3M	13M	13.6M	14.3M	14.9M	15.5M	16M	16.5M	...	21.3M	21.9M	22.5M	23.2M	23.8M	24.5M	25.1M	25.8M	26.5M	27.2M
194	South Africa	36.8M	37.7M	38.7M	39.6M	40.6M	41.4M	42.2M	43M	43.7M	...	49.1M	49.8M	50.5M	51.2M	52M	52.8M	53.7M	54.5M	55.4M	56.2M
195	Zambia	8.04M	8.25M	8.45M	8.66M	8.87M	9.1M	9.34M	9.6M	9.87M	...	12.5M	12.8M	13.2M	13.6M	14M	14.5M	14.9M	15.4M	15.9M	16.4M
196	Zimbabwe	10.4M	10.7M	10.9M	11.1M	11.3M	11.4M	11.5M	11.7M	11.7M	...	12.3M	12.4M	12.5M	12.7M	12.9M	13.1M	13.4M	13.6M	13.8M	14M

	country	1990	1991	1992	1993	1994	1995	1996	1997	1998	...	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
0	Afghanistan	1110	1010	971	665	493	728	690	656	627	...	1460	1480	1760	1960	1910	2080	2120	2100	2070	2060
1	Angola	1590	1650	1600	1240	1290	1520	1760	1940	2080	...	6920	7820	7750	7690	7680	8040	8140	8240	8040	7570
2	Albania	4840	3510	3280	3610	3930	4490	4930	4420	4840	...	9180	9940	10.3k	10.8k	11.1k	11.3k	11.4k	11.6k	11.9k	12.3k
3	Andorra	31.8k	31.3k	30.4k	29.1k	29k	29.2k	30.4k	33.2k	34.5k	...	48.6k	46.3k	46.7k	43.6k	46.9k	46.9k	48.9k	50.2k	52.1k	53.9k
4	United Arab Emirates	51.1k	50.3k	50.3k	49.8k	52.8k	55.9k	58.3k	62.5k	62.5k	...	76.6k	68.8k	58.4k	54.9k	56.1k	57.4k	59.9k	62.4k	65.2k	66.5k
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
190	Samoa	4060	3940	3890	4010	3870	4100	4370	4370	4450	...	6360	6380	6030	6010	6210	5910	5830	5790	6000	6450
191	Yemen	4170	4210	4320	4270	4340	4400	4440	4520	4650	...	5190	5230	5280	5540	4700	4690	4790	4660	3270	2880
192	South Africa	10.3k	9940	9490	9380	9450	9540	9760	9840	9730	...	12.4k	12.6k	12.3k	12.5k	12.7k	12.7k	12.9k	12.9k	12.8k	12.7k
193	Zambia	2190	2130	2050	2130	1900	1910	1980	2000	1930	...	2620	2750	2920	3130	3200	3340	3400	3450	3440	3470
194	Zimbabwe	3320	3430	3060	3030	3260	3230	3520	3580	3650	...	2130	1740	1930	2270	2560	2930	2940	2960	2960	2940

	country	1990	1991	1992	1993	1994	1995	1996	1997	1998	...	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
0	Afghanistan	1110.0	1010.0	971.0	665.0	493.0	728.0	690.0	656.0	627.0	...	1460.0	1480.0	1760.0	1960.0	1910.0	2080.0	2120.0	2100.0	2070.0	2060.0
1	Angola	1590.0	1650.0	1600.0	1240.0	1290.0	1520.0	1760.0	1940.0	2080.0	...	6920.0	7820.0	7750.0	7690.0	7680.0	8040.0	8140.0	8240.0	8040.0	7570.0
2	Albania	4840.0	3510.0	3280.0	3610.0	3930.0	4490.0	4930.0	4420.0	4840.0	...	9180.0	9940.0	10300.0	10800.0	11100.0	11300.0	11400.0	11600.0	11900.0	12300.0
3	Andorra	31800.0	31300.0	30400.0	29100.0	29000.0	29200.0	30400.0	33200.0	34500.0	...	48600.0	46300.0	46700.0	43600.0	46900.0	46900.0	48900.0	50200.0	52100.0	53900.0
4	United Arab Emirates	51100.0	50300.0	50300.0	49800.0	52800.0	55900.0	58300.0	62500.0	62500.0	...	76600.0	68800.0	58400.0	54900.0	56100.0	57400.0	59900.0	62400.0	65200.0	66500.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
190	Samoa	4060.0	3940.0	3890.0	4010.0	3870.0	4100.0	4370.0	4370.0	4450.0	...	6360.0	6380.0	6030.0	6010.0	6210.0	5910.0	5830.0	5790.0	6000.0	6450.0
191	Yemen	4170.0	4210.0	4320.0	4270.0	4340.0	4400.0	4440.0	4520.0	4650.0	...	5190.0	5230.0	5280.0	5540.0	4700.0	4690.0	4790.0	4660.0	3270.0	2880.0
192	South Africa	10300.0	9940.0	9490.0	9380.0	9450.0	9540.0	9760.0	9840.0	9730.0	...	12400.0	12600.0	12300.0	12500.0	12700.0	12700.0	12900.0	12900.0	12800.0	12700.0
193	Zambia	2190.0	2130.0	2050.0	2130.0	1900.0	1910.0	1980.0	2000.0	1930.0	...	2620.0	2750.0	2920.0	3130.0	3200.0	3340.0	3400.0	3450.0	3440.0	3470.0
194	Zimbabwe	3320.0	3430.0	3060.0	3030.0	3260.0	3230.0	3520.0	3580.0	3650.0	...	2130.0	1740.0	1930.0	2270.0	2560.0	2930.0	2940.0	2960.0	2960.0	2940.0

	country	1990	1991	1992	1993	1994	1995	1996	1997	1998	...	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
0	Afghanistan	2070.00	2200.00	2380.00	2600.00	2830.00	3020.00	3160.00	3270.00	3350.00	...	4910.00	4960.00	4990.00	4940.00	5020.00	5190.00	5560.00	5820.00	6060.00	6270.00
1	Angola	527.00	532.00	543.00	569.00	598.00	608.00	582.00	582.00	667.00	...	904.00	933.00	958.00	978.00	990.00	1010.00	1030.00	1050.00	1080.00	1090.00
2	Albania	160.00	182.00	201.00	221.00	239.00	267.00	295.00	327.00	338.00	...	82.70	77.50	67.50	68.40	68.50	68.50	68.70	68.90	69.20	69.50
3	Andorra	0.48	0.51	0.54	0.55	0.55	0.53	0.51	0.50	0.49	...	0.52	0.52	0.53	0.54	0.54	0.54	0.55	0.55	0.55	0.55
4	United Arab Emirates	31.40	31.60	32.20	33.20	34.40	35.80	37.10	38.50	40.20	...	81.70	94.90	109.00	122.00	129.00	132.00	133.00	133.00	133.00	132.00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
189	Samoa	7.44	7.55	7.65	7.72	7.78	7.81	7.89	7.97	8.01	...	7.74	7.71	7.77	7.85	7.88	7.92	7.98	8.02	8.05	8.02
190	Yemen	356.00	373.00	393.00	414.00	433.00	450.00	465.00	479.00	493.00	...	683.00	706.00	728.00	745.00	766.00	791.00	820.00	844.00	876.00	906.00
191	South Africa	17200.00	17700.00	18300.00	18300.00	18600.00	18500.00	18800.00	19800.00	20400.00	...	22700.00	21600.00	20800.00	20100.00	19400.00	18600.00	18300.00	18400.00	18800.00	19200.00
192	Zambia	439.00	483.00	526.00	570.00	621.00	679.00	745.00	810.00	885.00	...	1240.00	1280.00	1300.00	1330.00	1370.00	1380.00	1410.00	1430.00	1460.00	1500.00
193	Zimbabwe	617.00	564.00	564.00	576.00	607.00	679.00	713.00	787.00	893.00	...	1320.00	1360.00	1390.00	1410.00	1440.00	1450.00	1490.00	1540.00	1580.00	1650.00

	country	1990	1991	1992	1993	1994	1995	1996	1997	1998	...	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
0	Afghanistan	12400000.0	13300000.0	14500000.0	15800000.0	17100000.0	18100000.0	18900000.0	19400000.0	19700000.0	...	27100000.0	27700000.0	28400000.0	29200000.0	30100000.0	31200000.0	32300000.0	33400000.0	34400000.0	35400000.0
1	Angola	11800000.0	12200000.0	12700000.0	13100000.0	13500000.0	13900000.0	14400000.0	14900000.0	15400000.0	...	20900000.0	21700000.0	22500000.0	23400000.0	24200000.0	25100000.0	26000000.0	26900000.0	27900000.0	28800000.0
2	Albania	3290000.0	3280000.0	3250000.0	3200000.0	3150000.0	3110000.0	3100000.0	3100000.0	3110000.0	...	3030000.0	3000000.0	2970000.0	2950000.0	2930000.0	2910000.0	2900000.0	2900000.0	2890000.0	2890000.0
3	Andorra	54500.0	56700.0	58900.0	61000.0	62700.0	63900.0	64400.0	64300.0	64100.0	...	82700.0	83900.0	84500.0	84500.0	83700.0	82400.0	80800.0	79200.0	78000.0	77300.0
4	United Arab Emirates	1830000.0	1940000.0	2050000.0	2170000.0	2290000.0	2420000.0	2540000.0	2670000.0	2810000.0	...	6170000.0	7090000.0	7920000.0	8550000.0	8950000.0	9140000.0	9200000.0	9210000.0	9260000.0	9360000.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
192	Samoa	163000.0	164000.0	165000.0	167000.0	169000.0	170000.0	171000.0	172000.0	173000.0	...	182000.0	183000.0	185000.0	186000.0	187000.0	189000.0	191000.0	192000.0	194000.0	195000.0
193	Yemen	11700000.0	12300000.0	13000000.0	13600000.0	14300000.0	14900000.0	15500000.0	16000000.0	16500000.0	...	21300000.0	21900000.0	22500000.0	23200000.0	23800000.0	24500000.0	25100000.0	25800000.0	26500000.0	27200000.0
194	South Africa	36800000.0	37700000.0	38700000.0	39600000.0	40600000.0	41400000.0	42200000.0	43000000.0	43700000.0	...	49100000.0	49800000.0	50500000.0	51200000.0	52000000.0	52800000.0	53700000.0	54500000.0	55400000.0	56200000.0
195	Zambia	8040000.0	8250000.0	8450000.0	8660000.0	8870000.0	9100000.0	9340000.0	9600000.0	9870000.0	...	12500000.0	12800000.0	13200000.0	13600000.0	14000000.0	14500000.0	14900000.0	15400000.0	15900000.0	16400000.0
196	Zimbabwe	10400000.0	10700000.0	10900000.0	11100000.0	11300000.0	11400000.0	11500000.0	11700000.0	11700000.0	...	12300000.0	12400000.0	12500000.0	12700000.0	12900000.0	13100000.0	13400000.0	13600000.0	13800000.0	14000000.0

	country_Murder	1990_Per 100k	1991_Per 100k	1992_Per 100k	1993_Per 100k	1994_Per 100k	1995_Per 100k	1996_Per 100k	1997_Per 100k	1998_Per 100k	...	2007_Per 100k	2008_Per 100k	2009_Per 100k	2010_Per 100k	2011_Per 100k	2012_Per 100k	2013_Per 100k	2014_Per 100k	2015_Per 100k	2016_Per 100k
0	Afghanistan	16.693548	16.541353	16.413793	16.455696	16.549708	16.685083	16.719577	16.855670	17.005076	...	18.118081	17.906137	17.570423	16.917808	16.677741	16.634615	17.213622	17.425150	17.616279	17.711864
1	Angola	4.466102	4.360656	4.275591	4.343511	4.429630	4.374101	4.041667	3.906040	4.331169	...	4.325359	4.299539	4.257778	4.179487	4.090909	4.023904	3.961538	3.903346	3.870968	3.784722
2	Albania	4.863222	5.548780	6.184615	6.906250	7.587302	8.585209	9.516129	10.548387	10.868167	...	2.729373	2.583333	2.272727	2.318644	2.337884	2.353952	2.368966	2.375862	2.394464	2.404844
3	Andorra	0.880734	0.899471	0.916808	0.901639	0.877193	0.829421	0.791925	0.777605	0.764431	...	0.628779	0.619785	0.627219	0.639053	0.645161	0.655340	0.680693	0.694444	0.705128	0.711514
4	United Arab Emirates	1.715847	1.628866	1.570732	1.529954	1.502183	1.479339	1.460630	1.441948	1.430605	...	1.324149	1.338505	1.376263	1.426901	1.441341	1.444201	1.445652	1.444083	1.436285	1.410256
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
192	Zambia	269.325153	294.512195	318.787879	341.317365	367.455621	399.411765	435.672515	470.930233	511.560694	...	681.318681	699.453552	702.702703	715.053763	732.620321	730.158730	738.219895	744.791667	752.577320	769.230769
193	Zimbabwe	5.273504	4.585366	4.338462	4.235294	4.244755	4.557047	4.600000	4.918750	5.412121	...	6.197183	6.210046	6.177778	6.077586	6.050420	5.918367	5.936255	5.968992	5.962264	6.066176
194	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
195	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
196	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Years	Africa Eastern and Southern	Afghanistan	Africa Western and Central	Angola	Albania	Arab World	United Arab Emirates	Argentina	...	Virgin Islands (U.S.)	Vietnam	Vanuatu	World	Samoa	Yemen, Rep.	South Africa	Zambia	Zimbabwe
1990	0.000000	0.000	0.000000	0.000	0.000000	0.000000	0.000	0.000000	...	0.000	0.000	0.000	0.000000	0.000	0.000	0.000000	0.000000	0.000
1991	7.797012	10.649	4.415455	4.208	10.307000	11.624660	1.866	5.440000	...	12.351	2.092	1.676	4.799869	2.100	8.318	29.955000	18.900000	4.941
1992	7.838142	10.821	4.530574	4.208	30.014999	12.123597	1.842	6.360000	...	12.511	1.912	1.692	4.962862	2.384	8.310	29.980000	19.370001	4.993
1993	7.849445	10.723	4.546265	4.231	25.257999	12.859821	1.849	10.100000	...	12.703	1.969	1.734	5.230848	2.631	8.355	29.922001	19.700001	4.974
1994	7.839701	10.726	4.539152	4.162	20.840000	12.949263	1.806	11.760000	...	12.469	1.931	1.592	5.486189	3.035	8.340	29.889000	18.426001	4.960
1995	7.833286	11.179	4.525745	4.114	14.611000	13.317197	1.800	18.799999	...	12.133	1.899	1.742	5.625571	3.187	8.964	29.893999	16.806000	5.633
1996	7.841857	10.962	4.566774	4.097	13.931000	12.382483	1.900	17.110001	...	12.210	1.930	1.724	5.700035	3.474	9.590	29.874001	15.300000	6.251
1997	7.858703	10.783	4.602367	4.088	16.875999	11.948039	1.980	14.820000	...	12.121	2.870	1.684	5.703384	3.903	10.201	29.907000	13.644000	6.930
1998	7.810903	10.802	4.662650	4.072	20.047001	11.969959	2.137	12.650000	...	11.862	2.290	1.760	5.857247	4.175	10.812	29.947001	12.000000	6.460
1999	7.789680	10.809	4.863700	4.055	20.840000	12.109096	2.216	14.050000	...	11.497	2.330	1.784	5.969774	4.480	11.460	29.913000	12.441000	6.000
2000	7.724844	10.806	4.921423	4.030	19.028000	12.596101	2.250	15.000000	...	11.109	2.260	1.687	5.769081	4.664	11.558	29.879999	12.930000	5.688
2001	7.732945	10.809	4.865752	4.004	18.575001	12.469380	2.493	17.320000	...	11.083	2.760	1.875	5.856253	4.960	11.714	30.690001	13.509000	5.355
2002	7.959582	11.257	4.781385	3.961	17.895000	12.465001	2.648	19.590000	...	11.195	2.120	1.921	6.074849	5.096	11.840	33.290001	14.118000	5.062
2003	7.787965	11.141	4.751168	3.958	16.989000	12.390521	2.751	15.360000	...	11.364	2.250	1.738	6.164370	5.156	11.971	32.310001	14.695000	4.750
2004	7.310328	10.988	4.731880	3.916	16.309999	11.352673	2.907	13.520000	...	11.048	2.140	1.751	6.001752	5.217	12.097	29.450001	15.298000	4.390
2005	7.117658	11.217	4.727947	3.882	15.970000	11.195937	3.120	11.510000	...	10.964	2.095	1.734	5.898290	5.294	12.206	29.120001	15.900000	4.538
2006	6.989477	11.099	4.648872	3.858	15.630000	10.425838	2.937	10.080000	...	10.411	2.087	1.684	5.632652	5.422	12.366	28.340000	13.245000	4.681
2007	6.738765	11.301	4.627661	3.821	15.970000	10.027224	2.872	8.470000	...	10.191	2.030	1.795	5.416889	5.360	12.494	26.540001	10.587000	4.829
2008	6.271977	11.093	4.599393	3.793	13.060000	9.724742	2.737	7.840000	...	10.308	1.927	1.751	5.410319	5.581	12.621	22.410000	7.930000	5.014
2009	6.323909	11.311	4.583291	3.780	13.670000	9.329220	2.679	8.650000	...	11.584	1.740	1.808	6.005676	5.828	12.749	23.520000	10.558000	5.083
2010	6.867786	11.352	4.554662	9.430	14.090000	9.400912	2.481	7.710000	...	11.734	1.110	1.850	5.902056	5.728	12.831	24.680000	13.190000	5.209
2011	6.748081	11.054	4.548376	7.360	13.480000	10.476673	2.302	7.180000	...	12.080	1.000	1.811	5.765093	5.680	13.235	24.639999	10.551000	5.370
2012	6.562179	11.341	4.637602	7.347	13.380000	10.663129	2.185	7.220000	...	12.231	1.030	1.852	5.737713	8.750	13.167	24.730000	7.850000	5.153
2013	6.445456	11.193	4.410216	7.366	15.870000	10.735393	2.044	7.100000	...	12.755	1.320	1.862	5.727649	8.667	13.268	24.559999	8.611000	4.982
2014	6.405195	11.142	4.688088	7.372	18.049999	10.872227	1.911	7.270000	...	12.791	1.260	1.808	5.603743	8.720	13.470	24.889999	9.362000	4.770
2015	6.490041	11.127	4.626737	7.392	17.190001	10.965901	1.768	7.521000	...	12.617	1.850	1.862	5.620230	8.501	13.770	25.150000	10.125000	4.778
2016	6.610205	11.158	5.567017	7.412	15.420000	10.761115	1.640	8.111000	...	12.677	1.850	1.776	5.656575	8.307	13.433	26.540001	10.872000	4.788

Years	Afghanistan	Angola	Albania	Andorra	United Arab Emirates	Argentina	Armenia	American Samoa	Antigua and Barbuda	Australia	...	Uzbekistan	St. Vincent and the Grenadines	Venezuela	Vietnam	Vanuatu	Samoa	Yemen	South Africa	Zambia	Zimbabwe
1990	16.693548	4.466102	4.863222	0.880734	1.715847	6.012270	4.661017	4.944000	0.013000	4.702073	...	1.780583	0.369775	1.281746	6.715686	1.560748	0.037959	0.523529	11700.680272	269.325153	5.273504
1991	16.541353	4.360656	5.548780	0.899471	1.628866	5.981873	5.299145	4.984227	0.013372	4.568855	...	1.852427	0.386581	1.400000	6.746411	1.629630	0.037562	0.537464	11721.854305	294.512195	4.585366
1992	16.413793	4.275591	6.184615	0.916808	1.570732	6.059701	5.843023	4.961240	0.013851	4.444444	...	1.920233	0.403175	1.513619	6.728972	1.722222	0.037136	0.554302	11806.451613	318.787879	4.338462
1993	16.455696	4.343511	6.906250	0.901639	1.529954	6.088235	6.547619	4.878419	0.014091	4.334601	...	1.968811	0.411950	1.607692	6.712329	1.805556	0.036762	0.572614	11437.500000	341.317365	4.235294
1994	16.549708	4.429630	7.587302	0.877193	1.502183	6.191860	6.554878	4.836310	0.014663	4.301887	...	2.035225	0.425000	1.698473	6.726457	1.888889	0.036186	0.587517	11341.463415	367.455621	4.244755
1995	16.685083	4.374101	8.585209	0.829421	1.479339	6.149425	6.366460	4.788937	0.015500	4.280350	...	2.043222	0.431677	1.766038	6.710526	1.944444	0.035662	0.600801	11011.904762	399.411765	4.557047
1996	16.719577	4.041667	9.516129	0.791925	1.460630	6.193182	6.151420	4.772080	0.015440	4.226933	...	2.055336	0.430769	1.798507	6.724138	2.000000	0.035223	0.611038	10930.232558	435.672515	4.600000
1997	16.855670	3.906040	10.548387	0.777605	1.441948	6.134454	5.591054	4.755927	0.015815	4.209215	...	2.011952	0.440367	1.790441	6.694915	2.055556	0.034956	0.621271	11314.285714	470.930233	4.918750
1998	17.005076	4.331169	10.868167	0.764431	1.430605	6.094183	5.273312	4.631148	0.016452	4.203980	...	1.981891	0.458967	1.938182	6.666667	2.129630	0.034378	0.631242	11460.674157	511.560694	5.412121
1999	17.079208	4.150943	9.903846	0.745342	1.417508	6.219178	4.983819	4.564926	0.017021	4.161491	...	1.943205	0.447130	2.114695	6.680328	2.222222	0.033882	0.641772	11823.204420	548.275862	6.035503
2000	17.259615	4.097561	8.306709	0.718654	1.402556	6.368564	4.462541	4.644737	0.017000	4.089219	...	1.948770	0.439759	2.411348	6.653226	2.287037	0.032769	0.652065	12540.540541	571.839080	6.666667
2001	17.546296	4.106509	7.156550	0.683507	1.418182	6.407507	4.229508	4.766839	0.018698	3.962963	...	1.946281	0.459459	2.621053	6.653386	2.361111	0.032073	0.666667	12275.132275	582.857143	6.648045
2002	17.654867	4.108571	6.038339	0.642857	1.442529	6.419098	4.158416	4.853129	0.018711	3.862239	...	1.941667	0.477477	2.843206	6.574803	2.435185	0.031235	0.683436	12216.494845	596.590909	6.847826
2003	18.185654	4.232044	4.967949	0.628415	1.433962	6.089239	4.105960	4.817150	0.019643	3.728606	...	1.911765	0.496988	3.117241	6.536965	2.500000	0.030391	0.698663	12110.552764	621.468927	6.842105
2004	18.502024	4.351064	4.000000	0.642202	1.380835	5.792208	4.233333	4.694894	0.020503	3.576642	...	1.947034	0.536145	3.113014	6.475096	2.541284	0.029846	0.719615	11813.725490	636.871508	6.871795
2005	18.482490	4.355670	3.055016	0.633714	1.372549	5.655527	4.395973	4.601227	0.022277	3.539394	...	2.006397	0.563253	3.193220	6.401515	2.623853	0.029280	0.756563	11483.253589	655.555556	6.716418
2006	18.371212	4.358209	2.709150	0.629630	1.307547	5.674300	4.527027	4.437727	0.022488	3.606755	...	2.045064	0.573574	3.422819	6.455224	2.697248	0.028662	0.775414	11121.495327	674.033149	6.473430
2007	18.118081	4.325359	2.729373	0.628779	1.324149	5.566751	4.539249	4.285714	0.023636	3.622142	...	2.081897	0.600601	3.621262	6.544118	2.816514	0.028456	0.799766	10365.296804	681.318681	6.197183
2008	17.906137	4.299539	2.583333	0.619785	1.338505	5.685786	4.639175	4.180328	0.023286	3.681055	...	2.123377	0.592814	3.762376	6.666667	2.916667	0.027935	0.819026	9600.000000	699.453552	6.210046
2009	17.570423	4.257778	2.272727	0.627219	1.376263	5.753086	4.567474	4.175317	0.023991	3.691756	...	2.145652	0.605970	3.823529	6.548043	2.990741	0.027750	0.835821	9043.478261	702.702703	6.177778
2010	16.917808	4.179487	2.318644	0.639053	1.426901	5.843521	4.444444	4.193182	0.022207	3.662307	...	2.183406	0.619048	3.721683	6.491228	3.064815	0.027641	0.846591	8516.949153	715.053763	6.077586
2011	16.677741	4.090909	2.337884	0.645161	1.441341	5.956416	4.375000	4.188130	0.021556	3.609467	...	2.192982	0.635015	3.750000	6.379310	3.111111	0.027266	0.861642	7983.539095	732.620321	6.050420
2012	16.634615	4.023904	2.353952	0.655340	1.444201	6.076555	4.305556	4.214602	0.020917	3.576471	...	2.241758	0.642012	3.853503	6.326531	3.166667	0.026939	0.880846	7440.000000	730.158730	5.918367
2013	17.213622	3.961538	2.368966	0.680693	1.445652	6.232227	4.068966	4.273224	0.020558	3.492991	...	2.273731	0.660767	3.860759	6.220736	3.220183	0.026779	0.903084	7120.622568	738.219895	5.936255
2014	17.425150	3.903346	2.375862	0.694444	1.444083	6.291080	3.917526	4.330454	0.020508	3.503480	...	2.305987	0.673529	3.855799	6.118421	3.302752	0.026733	0.920393	6969.696970	744.791667	5.968992
2015	17.616279	3.870968	2.394464	0.705128	1.436285	6.148492	3.890785	4.369658	0.020293	3.525346	...	2.360802	0.674487	3.862928	6.051780	3.376147	0.026744	0.944984	6937.269373	752.577320	5.962264
2016	17.711864	3.784722	2.404844	0.711514	1.410256	6.000000	3.911565	4.380952	0.019959	3.542857	...	2.393736	0.660819	3.869969	5.955414	3.458716	0.026823	0.967949	6906.474820	769.230769	6.066176

Country	Aruba	Africa Eastern and Southern	Afghanistan	Africa Western and Central	Angola	Albania	Andorra	Arab World	United Arab Emirates	Argentina	...	Virgin Islands (U.S.)	Vietnam	Vanuatu	World	Samoa	Kosovo	Yemen, Rep.	South Africa	Zambia	Zimbabwe
1990	3.961402	0.050826	0.000000	6.562921	-3.450099	-9.575640	3.781388	12.373098	18.327986	-2.467214	...	0.000000	5.100918	11.695700	2.876178	-4.421451	0.000000	0.000000	-0.317786	-0.481072	6.988553
1991	7.962872	-0.095421	0.000000	1.121069	0.991359	-28.002142	2.546004	2.337089	0.860082	9.133111	...	0.000000	5.960844	3.147638	1.446386	-2.300009	0.000000	6.293494	-1.018220	-0.036133	5.531782
1992	5.882353	-2.343192	0.000000	2.693959	-5.838281	-7.187111	0.929214	5.163188	3.344945	7.937292	...	0.000000	8.646047	2.585414	2.056186	-0.199992	0.000000	8.207598	-2.137057	-1.730922	-9.015570
1993	7.307692	-1.089417	0.000000	-1.160468	-23.983417	9.559412	-1.031484	3.141588	1.261191	8.206979	...	0.000000	8.072731	0.735448	1.808395	4.099990	0.000000	4.001966	1.233520	6.797274	1.051459
1994	8.203903	2.051914	0.000000	-0.299641	1.339363	8.302867	2.383182	3.322772	6.896149	5.836201	...	0.000000	8.838981	9.081466	3.296975	-2.542099	0.000000	6.721949	3.200000	-8.625442	9.235199
1995	2.547144	4.409975	0.000000	1.927028	15.000000	13.322333	2.757502	2.990042	6.687886	-2.845210	...	0.000000	9.540480	1.003945	3.089415	6.673643	0.000000	5.669371	3.100000	2.897669	0.158026
1996	1.185788	5.570030	0.000000	4.629048	13.544370	9.099999	4.649739	4.693627	5.798404	5.526690	...	0.000000	9.340017	2.327335	3.613833	7.178972	0.000000	4.634967	4.300000	6.218546	10.360697
1997	7.046874	3.425412	0.000000	4.234699	7.274277	-10.919984	9.067672	4.323094	8.190399	8.111047	...	0.000000	8.152084	4.906813	3.869133	0.643422	0.000000	5.231112	2.600000	3.814007	2.680594
1998	1.991986	1.789507	0.000000	3.506516	4.691146	8.829424	3.194793	5.148232	0.291994	3.850179	...	0.000000	5.764455	1.176854	2.793154	2.194889	0.000000	6.006695	0.500000	-0.385746	2.885212
1999	1.238042	2.603876	0.000000	1.421036	2.181490	12.890804	4.099079	1.809676	2.902214	-3.385457	...	0.000000	4.773587	0.337293	3.504464	2.185433	0.000000	3.775530	2.400000	4.650190	-0.817821
2000	7.616588	3.197143	0.000000	3.734635	3.054624	6.946217	3.528362	6.599485	10.852704	-0.788999	...	0.000000	6.787316	5.924809	4.501278	6.918794	0.000000	6.181916	4.200000	3.897323	-3.059190
2001	-2.971257	3.526480	0.000000	5.212695	4.205999	8.293313	8.119358	1.684811	1.399085	-4.408840	...	0.000000	6.192893	-3.397582	2.000111	6.939762	0.000000	3.803646	2.700000	5.316868	1.439615
2002	-3.273646	3.992607	0.000000	9.899591	13.665687	4.536524	4.546362	0.605117	2.433457	-10.894485	...	0.000000	6.320821	-5.198319	2.337281	4.343996	0.000000	3.935232	3.700374	4.506014	-8.894024
2003	1.975547	2.908004	8.832278	5.518510	2.989850	5.528637	8.694204	4.713562	8.800541	8.837041	...	-0.396081	6.899063	4.288335	3.159531	4.515482	0.000000	3.747398	2.949075	6.944974	-16.995075
2004	7.911563	5.656582	1.414118	8.013486	10.952862	5.514668	8.135676	9.004155	9.566437	9.029573	...	3.285894	7.536411	3.987393	4.480070	4.625001	0.000000	3.972696	4.554560	7.032395	-5.807538
2005	1.214349	6.361804	11.229715	5.848351	15.028915	5.526424	5.397796	5.415001	4.855141	8.851660	...	3.485309	7.547248	5.305326	4.048361	4.156490	0.000000	5.591748	5.277052	7.235599	-5.711084
2006	1.050608	6.688755	5.357403	5.374463	11.547683	5.902659	4.808689	6.065760	9.837320	8.047152	...	3.504993	6.977955	8.465160	4.495723	1.968808	0.000000	3.170409	5.603806	7.903694	-3.461495
2007	1.800226	6.857304	13.826320	5.530987	14.010018	5.983260	1.553188	4.524154	3.184390	9.007651	...	4.010594	7.129504	2.871660	4.438864	6.322645	0.000000	3.338428	5.360474	8.352436	-3.653327
2008	-0.090708	4.572539	3.924984	6.279223	11.166138	7.500041	-5.559186	5.773995	3.191836	4.057233	...	1.218625	5.661771	5.602991	2.000950	1.009088	0.000000	3.647569	3.191044	7.773896	-17.668946
2009	-10.519749	0.946811	21.390528	6.274463	0.858713	3.354289	-5.302847	0.643073	-5.242922	-5.918525	...	-6.594789	5.397898	3.037246	-1.307019	-4.808274	5.034884	3.866230	-1.538089	9.220348	12.019560
2010	-3.685029	5.152336	14.362441	6.957010	4.403933	3.706938	-1.974958	4.776624	1.602850	10.125398	...	0.596383	6.423238	1.260682	4.494860	0.467246	4.939924	7.702307	3.039733	10.298223	19.675323
2011	3.446055	4.014183	0.426355	4.848351	3.471976	2.545406	-0.008070	3.806778	6.928509	6.003952	...	-8.204246	6.240303	3.137874	3.339731	4.103641	6.319886	-12.714823	3.168556	5.564602	14.193913
2012	-1.369863	1.972652	12.752287	5.142964	8.542188	1.417243	-4.974444	5.041890	4.483792	-1.026420	...	-14.812500	5.247367	1.010030	2.673060	-4.084494	1.712195	2.392886	2.396232	7.597593	16.665429
2013	4.198232	4.308370	5.600745	6.104241	4.954545	1.002018	-3.547597	2.965049	5.053078	2.405324	...	-6.285155	5.421883	0.468608	2.844854	-0.342327	5.340908	4.823415	2.485468	5.057232	1.989493
2014	0.300000	3.986754	2.724543	5.927350	4.822628	1.774449	2.504466	2.557937	4.410085	-2.512615	...	-1.774530	5.983655	3.126246	3.117863	0.052171	3.348804	-0.188574	1.413826	4.697992	2.376929
2015	5.700001	2.925591	1.451315	2.745937	0.943572	2.218726	1.434140	3.002276	5.060335	2.731160	...	-0.425080	6.679289	0.371578	3.168373	4.296881	5.916270	-27.994546	1.321862	2.920375	1.779873
2016	2.100000	2.019391	2.260314	0.127595	-2.580050	3.314981	3.709678	3.200034	2.984216	-2.080328	...	1.600854	6.210812	4.688678	2.825118	8.126249	5.571757	-9.375124	0.664552	3.776679	0.755869

Years	Aruba	Africa Eastern and Southern	Afghanistan	Africa Western and Central	Angola	Albania	Andorra	Arab World	United Arab Emirates	Argentina	...	Virgin Islands (U.S.)	Vietnam	Vanuatu	World	Samoa	Kosovo	Yemen, Rep.	South Africa	Zambia	Zimbabwe
1990	3.961402	0.050826	0.000000	6.562921	-3.450099	-9.575640	3.781388	12.373098	18.327986	-2.467214	...	0.000000	5.100918	11.695700	2.876178	-4.421451	0.000000	0.000000	-0.317786	-0.481072	6.988553
1991	7.962872	-0.095421	0.000000	1.121069	0.991359	-28.002142	2.546004	2.337089	0.860082	9.133111	...	0.000000	5.960844	3.147638	1.446386	-2.300009	0.000000	6.293494	-1.018220	-0.036133	5.531782
1992	5.882353	-2.343192	0.000000	2.693959	-5.838281	-7.187111	0.929214	5.163188	3.344945	7.937292	...	0.000000	8.646047	2.585414	2.056186	-0.199992	0.000000	8.207598	-2.137057	-1.730922	-9.015570
1993	7.307692	-1.089417	0.000000	-1.160468	-23.983417	9.559412	-1.031484	3.141588	1.261191	8.206979	...	0.000000	8.072731	0.735448	1.808395	4.099990	0.000000	4.001966	1.233520	6.797274	1.051459
1994	8.203903	2.051914	0.000000	-0.299641	1.339363	8.302867	2.383182	3.322772	6.896149	5.836201	...	0.000000	8.838981	9.081466	3.296975	-2.542099	0.000000	6.721949	3.200000	-8.625442	9.235199
1995	2.547144	4.409975	0.000000	1.927028	15.000000	13.322333	2.757502	2.990042	6.687886	-2.845210	...	0.000000	9.540480	1.003945	3.089415	6.673643	0.000000	5.669371	3.100000	2.897669	0.158026
1996	1.185788	5.570030	0.000000	4.629048	13.544370	9.099999	4.649739	4.693627	5.798404	5.526690	...	0.000000	9.340017	2.327335	3.613833	7.178972	0.000000	4.634967	4.300000	6.218546	10.360697
1997	7.046874	3.425412	0.000000	4.234699	7.274277	-10.919984	9.067672	4.323094	8.190399	8.111047	...	0.000000	8.152084	4.906813	3.869133	0.643422	0.000000	5.231112	2.600000	3.814007	2.680594
1998	1.991986	1.789507	0.000000	3.506516	4.691146	8.829424	3.194793	5.148232	0.291994	3.850179	...	0.000000	5.764455	1.176854	2.793154	2.194889	0.000000	6.006695	0.500000	-0.385746	2.885212
1999	1.238042	2.603876	0.000000	1.421036	2.181490	12.890804	4.099079	1.809676	2.902214	-3.385457	...	0.000000	4.773587	0.337293	3.504464	2.185433	0.000000	3.775530	2.400000	4.650190	-0.817821
2000	7.616588	3.197143	0.000000	3.734635	3.054624	6.946217	3.528362	6.599485	10.852704	-0.788999	...	0.000000	6.787316	5.924809	4.501278	6.918794	0.000000	6.181916	4.200000	3.897323	-3.059190
2001	-2.971257	3.526480	0.000000	5.212695	4.205999	8.293313	8.119358	1.684811	1.399085	-4.408840	...	0.000000	6.192893	-3.397582	2.000111	6.939762	0.000000	3.803646	2.700000	5.316868	1.439615
2002	-3.273646	3.992607	0.000000	9.899591	13.665687	4.536524	4.546362	0.605117	2.433457	-10.894485	...	0.000000	6.320821	-5.198319	2.337281	4.343996	0.000000	3.935232	3.700374	4.506014	-8.894024
2003	1.975547	2.908004	8.832278	5.518510	2.989850	5.528637	8.694204	4.713562	8.800541	8.837041	...	-0.396081	6.899063	4.288335	3.159531	4.515482	0.000000	3.747398	2.949075	6.944974	-16.995075
2004	7.911563	5.656582	1.414118	8.013486	10.952862	5.514668	8.135676	9.004155	9.566437	9.029573	...	3.285894	7.536411	3.987393	4.480070	4.625001	0.000000	3.972696	4.554560	7.032395	-5.807538
2005	1.214349	6.361804	11.229715	5.848351	15.028915	5.526424	5.397796	5.415001	4.855141	8.851660	...	3.485309	7.547248	5.305326	4.048361	4.156490	0.000000	5.591748	5.277052	7.235599	-5.711084
2006	1.050608	6.688755	5.357403	5.374463	11.547683	5.902659	4.808689	6.065760	9.837320	8.047152	...	3.504993	6.977955	8.465160	4.495723	1.968808	0.000000	3.170409	5.603806	7.903694	-3.461495
2007	1.800226	6.857304	13.826320	5.530987	14.010018	5.983260	1.553188	4.524154	3.184390	9.007651	...	4.010594	7.129504	2.871660	4.438864	6.322645	0.000000	3.338428	5.360474	8.352436	-3.653327
2008	-0.090708	4.572539	3.924984	6.279223	11.166138	7.500041	-5.559186	5.773995	3.191836	4.057233	...	1.218625	5.661771	5.602991	2.000950	1.009088	0.000000	3.647569	3.191044	7.773896	-17.668946
2009	-10.519749	0.946811	21.390528	6.274463	0.858713	3.354289	-5.302847	0.643073	-5.242922	-5.918525	...	-6.594789	5.397898	3.037246	-1.307019	-4.808274	5.034884	3.866230	-1.538089	9.220348	12.019560
2010	-3.685029	5.152336	14.362441	6.957010	4.403933	3.706938	-1.974958	4.776624	1.602850	10.125398	...	0.596383	6.423238	1.260682	4.494860	0.467246	4.939924	7.702307	3.039733	10.298223	19.675323
2011	3.446055	4.014183	0.426355	4.848351	3.471976	2.545406	-0.008070	3.806778	6.928509	6.003952	...	-8.204246	6.240303	3.137874	3.339731	4.103641	6.319886	-12.714823	3.168556	5.564602	14.193913
2012	-1.369863	1.972652	12.752287	5.142964	8.542188	1.417243	-4.974444	5.041890	4.483792	-1.026420	...	-14.812500	5.247367	1.010030	2.673060	-4.084494	1.712195	2.392886	2.396232	7.597593	16.665429
2013	4.198232	4.308370	5.600745	6.104241	4.954545	1.002018	-3.547597	2.965049	5.053078	2.405324	...	-6.285155	5.421883	0.468608	2.844854	-0.342327	5.340908	4.823415	2.485468	5.057232	1.989493
2014	0.300000	3.986754	2.724543	5.927350	4.822628	1.774449	2.504466	2.557937	4.410085	-2.512615	...	-1.774530	5.983655	3.126246	3.117863	0.052171	3.348804	-0.188574	1.413826	4.697992	2.376929
2015	5.700001	2.925591	1.451315	2.745937	0.943572	2.218726	1.434140	3.002276	5.060335	2.731160	...	-0.425080	6.679289	0.371578	3.168373	4.296881	5.916270	-27.994546	1.321862	2.920375	1.779873
2016	2.100000	2.019391	2.260314	0.127595	-2.580050	3.314981	3.709678	3.200034	2.984216	-2.080328	...	1.600854	6.210812	4.688678	2.825118	8.126249	5.571757	-9.375124	0.664552	3.776679	0.755869

country	Afghanistan	Angola	Albania	Andorra	United Arab Emirates	Argentina	Armenia	Antigua and Barbuda	Australia	Austria	...	Uzbekistan	St. Vincent and the Grenadines	Venezuela	Vietnam	Vanuatu	Samoa	Yemen	South Africa	Zambia	Zimbabwe
1990	1110.0	1590.0	4840.0	31800.0	51100.0	14100.0	5180.0	15500.0	31200.0	37600.0	...	3420.0	6380.0	15200.0	1670.0	2890.0	4060.0	4170.0	10300.0	2190.0	3320.0
1991	1010.0	1650.0	3510.0	31300.0	50300.0	15200.0	4620.0	15600.0	30600.0	38500.0	...	3330.0	6430.0	16400.0	1740.0	2900.0	3940.0	4210.0	9940.0	2130.0	3430.0
1992	971.0	1600.0	3280.0	30400.0	50300.0	16200.0	2740.0	15500.0	30400.0	38800.0	...	2890.0	6830.0	17100.0	1850.0	2890.0	3890.0	4320.0	9490.0	2050.0	3060.0
1993	665.0	1240.0	3610.0	29100.0	49800.0	17300.0	2550.0	16000.0	31300.0	38700.0	...	2760.0	7120.0	16900.0	1960.0	2830.0	4010.0	4270.0	9380.0	2130.0	3030.0
1994	493.0	1290.0	3930.0	29000.0	52800.0	18100.0	2760.0	16700.0	32200.0	39500.0	...	2560.0	7030.0	16300.0	2090.0	3000.0	3870.0	4340.0	9450.0	1900.0	3260.0
1995	728.0	1520.0	4490.0	29200.0	55900.0	17400.0	3010.0	15600.0	33000.0	40500.0	...	2490.0	7570.0	16600.0	2250.0	2960.0	4100.0	4400.0	9540.0	1910.0	3230.0
1996	690.0	1760.0	4930.0	30400.0	58300.0	18100.0	3230.0	16300.0	33900.0	41400.0	...	2490.0	7670.0	16300.0	2430.0	2960.0	4370.0	4440.0	9760.0	1980.0	3520.0
1997	656.0	1940.0	4420.0	33200.0	62500.0	19300.0	3380.0	16800.0	34800.0	42200.0	...	2570.0	7950.0	17100.0	2590.0	3050.0	4370.0	4520.0	9840.0	2000.0	3580.0
1998	627.0	2080.0	4840.0	34500.0	62500.0	19900.0	3650.0	17300.0	36100.0	43700.0	...	2640.0	8280.0	17000.0	2700.0	3030.0	4450.0	4650.0	9730.0	1930.0	3650.0
1999	597.0	2190.0	5490.0	35700.0	64300.0	19000.0	3800.0	17600.0	37500.0	45100.0	...	2720.0	8510.0	15700.0	2800.0	2990.0	4520.0	4690.0	9820.0	1970.0	3600.0
2000	578.0	2320.0	5910.0	35400.0	71200.0	18600.0	4050.0	18300.0	38500.0	46600.0	...	2790.0	8650.0	16100.0	2960.0	3100.0	4810.0	4850.0	10100.0	1990.0	3470.0
2001	563.0	2490.0	6460.0	35500.0	71800.0	17600.0	4460.0	17200.0	38700.0	47000.0	...	2870.0	8790.0	16400.0	3110.0	2930.0	5120.0	4890.0	10200.0	2040.0	3510.0
2002	1190.0	2900.0	6780.0	35600.0	73000.0	15500.0	5080.0	17100.0	39700.0	47500.0	...	2940.0	9320.0	14800.0	3270.0	2710.0	5310.0	4940.0	10500.0	2080.0	3190.0
2003	1240.0	3120.0	7180.0	38600.0	78100.0	16700.0	5820.0	18000.0	40400.0	47700.0	...	3030.0	10000.0	13500.0	3460.0	2760.0	5520.0	4980.0	10600.0	2170.0	2640.0
2004	1200.0	3520.0	7610.0	40600.0	82000.0	18000.0	6470.0	18700.0	41600.0	48700.0	...	3220.0	10400.0	15800.0	3690.0	2790.0	5740.0	5030.0	11000.0	2260.0	2480.0
2005	1290.0	4270.0	8070.0	44500.0	80200.0	19400.0	7420.0	19700.0	42300.0	49500.0	...	3410.0	10700.0	17200.0	3930.0	2870.0	5940.0	5160.0	11400.0	2360.0	2330.0
2006	1320.0	5560.0	8600.0	47800.0	86400.0	20800.0	8460.0	21900.0	42900.0	50900.0	...	3620.0	11500.0	18700.0	4170.0	3040.0	6020.0	5170.0	11900.0	2480.0	2230.0
2007	1460.0	6920.0	9180.0	48600.0	76600.0	22400.0	9710.0	23500.0	44300.0	52700.0	...	3900.0	11900.0	20000.0	4420.0	3120.0	6360.0	5190.0	12400.0	2620.0	2130.0
2008	1480.0	7820.0	9940.0	46300.0	68800.0	23100.0	10500.0	23100.0	45000.0	53300.0	...	4190.0	12100.0	20700.0	4630.0	3240.0	6380.0	5230.0	12600.0	2750.0	1740.0
2009	1760.0	7750.0	10300.0	46700.0	58400.0	21500.0	9050.0	20000.0	44900.0	51100.0	...	4450.0	11800.0	19800.0	4830.0	3270.0	6030.0	5280.0	12300.0	2920.0	1930.0
2010	1960.0	7690.0	10800.0	43600.0	54900.0	23500.0	9290.0	18200.0	45100.0	51900.0	...	4650.0	11400.0	19700.0	5090.0	3240.0	6010.0	5540.0	12500.0	3130.0	2270.0
2011	1910.0	7680.0	11100.0	46900.0	56100.0	24600.0	9730.0	17600.0	45600.0	53300.0	...	4880.0	11400.0	20400.0	5350.0	3190.0	6210.0	4700.0	12700.0	3200.0	2560.0
2012	2080.0	8040.0	11300.0	46900.0	57400.0	24100.0	10400.0	18000.0	46600.0	53400.0	...	5160.0	11500.0	21300.0	5570.0	3160.0	5910.0	4690.0	12700.0	3340.0	2930.0
2013	2120.0	8140.0	11400.0	48900.0	59900.0	24400.0	10700.0	17600.0	47000.0	53100.0	...	5470.0	11700.0	24600.0	5820.0	3130.0	5830.0	4790.0	12900.0	3400.0	2940.0
2014	2100.0	8240.0	11600.0	50200.0	62400.0	23600.0	11000.0	18100.0	47400.0	53000.0	...	5760.0	11800.0	23400.0	6100.0	3120.0	5790.0	4660.0	12900.0	3450.0	2960.0
2015	2070.0	8040.0	11900.0	52100.0	65200.0	23900.0	11300.0	18600.0	47800.0	53000.0	...	6090.0	12000.0	21600.0	6440.0	3040.0	6000.0	3270.0	12800.0	3440.0	2960.0
2016	2060.0	7570.0	12300.0	53900.0	66500.0	23200.0	11300.0	19400.0	48300.0	53400.0	...	6350.0	12200.0	17500.0	6770.0	3060.0	6450.0	2880.0	12700.0	3470.0	2940.0

Project: Investigate a Dataset (Gapminder World)¶

Table of Contents¶

Introduction¶

We will explore these indicators in this data investigation ...¶

Data Wrangling¶

Exploring the collected data ...¶

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Functions¶

This Function replace K , M , B With thousand , million , billion.. as numbers ....¶

Droping all years except years from 1990 till 2016 ..¶

This function will return only the data in 2000, 2005, 2010 and 2016 , it will help me in visualizing my data since there are alot of years ...¶

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Data Cleaning ..¶

Checking for Nan Values in each data ..¶

Removing NaN values from Unemployment and GDP dataframes..¶

Rechecking forr Nan Values ..¶

Droping all the other years except years from 1990 till 2016 ..¶

Replacing K , M , B with numbers ...¶

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Joining Total murder data with population data to calculate the murder rate ...¶

Here i joined the total murder data to the right side of the population data and i marked its years by adding _Murder_counts to them .. in order to be able to calculate the murder ratio .... Murder ratio = total murder / population * 100k per population ...¶

Removing NaN rows in The DataFrame...¶

Checking if There is any NaN Values..¶

Making the Country column my indeces ...¶

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Working on the GDP Data ...¶

Making the Country column my indeces ...¶

Transposing the columns with the indeces¶

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Working on the Income Data ...¶

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Working on UnEmployment Dataframe ...¶

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Exploratory Data Analysis¶

I Found it hard to compare the data bcz its big ... So i decided to visualize few countries alone like (Russia , Egypt , Afghanistan, China, Japan, and United States ..¶

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Murder Rate Data ..¶

Egypt¶

Understanding the Chart ..¶

As we can see from the chart that murder rate in Egypt increases with an average of 1.125 every 5 Years ....¶

Exploring more details ..¶

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Japan¶

Understanding the Chart ..¶

As we can see from the chart that murder rate in Japan decreases with an average of 2.515 every 5 Years ....¶

Exploring more details ..¶

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Russia¶

Understanding the Chart ..¶

As we can see from the chart that murder rate in Russia increases with an average of 166.292 every 5 Years ....¶

Exploring more details ..¶

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Afghanistan¶

Understanding the Chart ..¶

As we can see from the chart that murder rate in Afghanistan increases and decreases with an average of 17.5925 every 5 Years ....¶

Exploring more details ..¶

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United States¶

Understanding the Chart ..¶

As we can see from the chart that murder rate in USA decreases with an average of 61.04 every 5 Years ....¶

Exploring more details ..¶

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China¶

Understanding the Chart ..¶

As we can see from the chart that murder rate in China decreases with an average of 163.535 every 5 Years ....¶

Exploring more details ..¶

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Question 1 : Which country has the most and least Murder rate?¶

Answer :¶

Calulating the countries that has the most and least Murder rate ...¶

Which Country has the most murder Rate?¶

Which Country has the Least murder Rate?¶

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Population rate¶

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Egypt¶

Understanding the Chart ..¶

As we can see from the chart that Egypt population has increased 25M person in only 16 years...¶

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